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CE-UV/LIF Analysis of Organic Fluorescent Dyes for Detection of Nanoplastics in Water Quality Testing

DOI: 10.31038/NAMS.2024724

Abstract

Nanoplastics in the environment is rarely monitored due to the current limitation of detection technology and research strategies. Capillary electrophoresis (CE) can be coupled with ultraviolet (UV) and laser-induced fluorescence (LIF) detection for the analysis of fluorescent rhodamine dyes with high sensitivity. These organic dyes interact with polystyrene nanoplastics present in a water sample to undergo adsorption. A decrease of CE-LIF peak height represents a loss of dye concentration due to binding with the nanosphere surfaces. A standard calibration curve has been constructed for CE-LIF analysis of polystyrene nanosphere standard solutions using a rhodamine 6G concentration of 125 µg/mL, background electrolyte solution of 10 mM Na₂HPO₄ at pH 5.0, electrokinetic sample injection at 18 kV for 6 s, applied voltage of 18 kV across the total capillary length of 68 cm, diode laser operating at 8 V, λ_ex at 480 nm, λ_em at 580 nm, and avalanche photosensor reverse-biased at 60 V. The fused silica capillary, after being conditioned with the background electrolyte solution for 30 min each day, yields good peak shapes, reproducible peak heights, and only slight variations in migration time. Each CE-analysis is completed within 10 min. Experimental binding data for rhody dye is modelled on the linear Langmuir isotherm equation to determine an adsorption capacity of 27-30 mg/g of nanospheres. The Freundlich isotherm model returns a similar adsorption capacity of 22 mg/g. The detection limit is 0.1 µg of polystyrene nanospheres in 1.6 mL of water sample for CE-LIF analysis.

Keywords

Binding isotherms, Capillary electrophoresis, Laser-induced fluorescence, Nanoplastics, Polystyrene, Rhodamine dyes, UV detection

Introduction

Nanoplastics are a group of synthetic polymer materials on the nanoscale size between 1 and 100 nm. Nanoplastics are primarily produced in laundry wastewater as acrylate, nylon, and polyester fibers [1]. They are normally present as colloids, and so their fate is governed by interfacial properties [2]. Incidentally produced nanoplastics exhibit a diversity of chemical compositions (most commonly polystyrene, polypropylene and polyethylene terephthalate) and physical morphologies that is typically absent from engineered nanomaterials [3]. Such diversity means that it is never straightforward to quantitively analyze water for an assessment of all suspended nanoplastics [4]. The contamination of freshwater lakes and rivers by nanoplastics represents an emerging global issue regarding their potential risk to aquatic life in these important ecosystems and flora, fauna, wildlife, and humans downstream. Pollution associated with nanoplastics can be tackled through source reduction, circular economy, and waste management [5]. Current water treatment processes are ineffective at removing nanoplastics; unlike microplastics, they are too small to be captured by conventional filtration systems. Their small size range enables nanoplastics to easily escape standard water separation and purification techniques [6,7]. The occurrence of microplastics in six major European rivers and their tributaries was investigated and reviewed based on the results from environmental studies that assessed the abundance of microplastics in different water columns [8]. Release of nanoplastics from drinking water bottles was characterized by SEM, XPS, SPES and µ-Raman Analysis [9]. Spherical organic nanoparticles from bottled water were collected effectively through a tangential flow ultrafiltration system [10]. Polyethylene terephthalate nanoplastics collected from commercially bottled drinking water were detected with an average mean size of 88 nm; their concentration was estimated to be 10⁸ particles/mL by nanoparticle tracking analysis [11]. A new study has reported the levels of micro- and nano-particles released in carbonated beverage bottles range from 68 to 4.7×10⁸ particles/L, potentially posing health risks to humans. Polypropylene bottles released more particles than polyethylene terephthalate and polyethylene bottles [12]. The occurrence of micro- and nano-plastics (with particle diameters from 0.7 to 20 μm) in plastic bottled water has been assessed, and the median concentration was 359 ng L⁻¹. Polyethylene was the most detected polymer, while polyethylene terephthalate was found at the highest concentrations [13]. The content of microplastic and nanoplastic particles in raw water, tap water, and drinking water was analyzed. Plastic particles were found in all water samples, with an average abundance ranging from 204 to 336 particles/L in raw water, from 22 to 33 particles/L in tap water, and from 25 to 73 particles/L in drinking water [14]. Pyrolysis gas chromatography–mass spectrometry allows for the simultaneous identification and quantification of nine nanoplastic types, including polyethylene terephthalate, polyethylene, polycarbonate, polypropylene, polymethyl methacrylate, polystyrene, polyvinylchloride, nylon 6, and nylon 66, in environmental and potable water samples based on polymer-specific mass concentration. Limits of quantification ranged from 0.01 to 0.44 µg/L [15]. The lower microplastics abundance in tap water than in natural sources indicates their removal in drinking water treatment plants [16]. This evidence should encourage the consumers to drink tap water instead of bottled water, to limit their exposure to micro- and nano-plastics. More than one hundred studies on microplastics in food, water, and beverages were reviewed by Vitali et al [17].

It is difficult to categorically state the detrimental effects of nanoplastics due to the nascent stage of their characterization in aquatic environments. The toxic effects of nanoplastics on living organisms have systematically been reviewed [18,19], and studied [20]. Potential interactions of nanoplastics with other substances in a complex water matrix could lead to improper quantification. Nanoplastics have been reported to bind with several types of organic contaminants in water environments due to their high surface area-to-volume ratio and the nature of their surfaces. These contaminants include polycyclic aromatic hydrocarbons, polychlorinated biphenyls, pharmaceuticals, heavy metal ions, fly ash, bisphenol A, antibiotics, and ammonium nitrogen. Inadvertent release of additives and contaminants adsorbed on nanoplastics in organism bodies poses more significant threats to living organisms than the nanoplastics themselves. New scientific evidence suggests that nanoplastics can attach to bacteria and viruses. In summary, this interplay of nanoplastics and water contaminants adds another layer of implications to quantitative analysis. In real exposure scenarios, formation of bio- and eco-coronas on nanoplastics is inevitable and displays various complex structures. Complete degradation of nanoplastics dispersed in water and exposed to simulated sunlight takes about a month for polystyrene and 2 years for polyethylene. These findings highlight the pervasiveness of nanoplastic pollution in our environment and underscore the importance of new research into detection methods. Modern instrumental methods for nanoplastics analysis (such as dark-field hyperspectral microscopy, micro-Fourier transform infrared imaging, surface enhanced Raman scattering/imaging, fluorescence microscopy, and atomic force microscopy) demonstrate many drawbacks including analysis time, availability, costs, detection limit, matrix digestion, and sample pretreatment. Although a LOD of 5 ppm was achieved in bottled water, tap water, and river water, single polystyrene nanoplastic particles can be only visualized down to 200 nm on the substrate. The treatment of environmental water samples is a particular challenge, due to their matrix complexity. Reliable techniques are lacking for isolating and pre-concentrating nanoplastics. It is crucial to integrate sample preparation regarding matrix effects into the development of any new instrumental method for nanoplastics analysis [21-39].

One feasible approach to the detection of nanoplastics with a substantial heterogeneity involves the addition of an organic fluorescent dye that interacts with their surfaces. Any binding can be determined, in principle, by a quantitative analysis of the dye before and after interaction with the nanoplastic to obtain a meaningful % binding result. The choice of organic dyes appropriate for binding plays a crucial part in the analysis of nanoparticles and nanoplastics in water. Absorption of ultraviolet (UV) light is a universal detection mode for organic compounds containing one or more aromatic rings. Better analytical sensitivity and selectivity can be expected from dyes that absorb the output wavelength of a laser and emit at a characteristic fluorescence wavelength for laser-induced fluorescence (LIF) detection. Capillary electrophoresis (CE) is an analytical separation technique that relies on the use of a fused silica capillary filled with a background electrolyte (BGE) solution to separate the dye from any interfering organic compounds. The capillary is operating under an applied voltage, in the kV range, between a positive cathode at the inlet and a negative anode at the outlet. Positively charged dyes will migrate rapidly towards the point of detection, being separated from each other due to differences in electrophoretic mobility. Neutral dyes will be transported by the forward electroosmotic flow, as a bundle without separation, through the capillary. Negatively charged dyes will take longer migration times to reach the detection point if their electrophoretic mobility in the reverse direction is not as high as the electroosmotic mobility. High detection selectivity is guaranteed if using fluorescence dyes that can be excited by a 480-nm diode laser. The binding analysis would be reliable if a mixture of organic dyes is employed to test the binding properties of each type of nanoplastic in water under controlled conditions of pH, ionic strength, and modifier.

This work aims at the development of a CE-LIF method for the analysis of dye mixtures, towards the quantitative analysis of nanoplastics without interference by other types of nanoparticles. Rhodamine is well documented in the scientific literature as the basis of chemosensors with colorimetric and fluorometric signals for the rapid detection of various metal ions, organic molecules, and biomolecules [40-43]. This dye becomes strongly emissive with versatile colors (red, orange, or purple), especially when the spirolactam ring is opened by a chelation mechanism. Among different rhodamine moieties, rhodamine B and 6G are very commonly used.

They offer unique optical properties including high photostability, large Stokes shift, and tunable fluorescence with structural derivatization of the side arms.

Materials and Methods

Mesityl oxide (MO), 4-dicyanomethylene-2-methyl-6-(4-dimethylaminostyryl)-4H-pyran (DCM), disodium fluorescein (DF), fluorescein adenosine triphosphate (FATP), rhodamine 6G hydrochloride (R6G·HCl), rhodamine B, and sodium phosphate dibasic (Na₂HPO4) were obtained from Millipore Sigma (Oakville, Ontario, Canada). Invitrogen fluorescent dyes, coumarin 503 and coumarin 540A, were sourced from ThermoFisher Scientific (Waltham, Massachusetts, USA). Polystyrene 3080A nanospheres with an average diameter of 81 ± 3 nm were supplied by ThermoFisher Scientific (Fremont, California, USA).

To prepare the 10 mM background electrolyte (BGE) solution, 0.284 g of Na₂HPO4 was accurately weighed and dissolved in 150 mL of distilled deionized water (DDW) in a 200 mL conical flask. The solution was stirred continuously until Na₂HPO4 fully dissolved. The pH was adjusted to 5.0 by the careful addition (three drops) of concentrated hydrochloric acid (HCl, 37% w/w) using a digital pipette for precision. After each acid increment, the solution was stirred, and the pH was checked and adjusted as necessary. The final volume was brought up to 200 mL with DDW to achieve the intended concentration. The prepared BGE (pH 5.0) was then stored in a clean container and rechecked for pH consistency before use.The CE-UV/LIF setup consisted of an SRI Model 203 chromatography data system box (Las Vegas, USA) acting as both the controller for the high-voltage power supply and a station for converting the detector output voltage into a digital signal, acquired by PeakSimple software. The UV detection employed a Bischoff Lambda 1010 detector (Metrohm Herisau, Switzerland), while the LIF system comprised a 480-nm diode laser paired with a Hamamatsu H7827 series photosensor module (Iwata City, Japan). Rhodamine B and R6G were prepared at a concentration of 5 mg in 2 mL of methanol or distilled water, ensuring effective fluorescence intensity, even in an acidic medium. Samples were introduced by electrokinetic injection at 18 kV for 6 seconds. A fused silica capillary, preconditioned with NaOH, distilled water and BGE for 30 minutes, was used for the CE analysis, which typically ran at 18 kV for 40 minutes. The percentage binding was calculated using the formula: (initial peak height – final peak height)/initial peak height = decrease in peak height/initial peak height. The LIF detector’s 480-nm laser employed a low applied voltage setting of 4.5 V to remove the polyimide coating within 1 second, creating a 1.0-mm clear window on a new capillary. All standard fluorescence excitation/emission spectra were recorded using a Horiba Fluoromax-4 spectrometer (Burlington, Ontario, Canada). The study of the effect of pH on the percentage binding of rhodamine B dye with polystyrene nanospheres was conducted to optimize sensitivity. The BGE’s pH was adjusted to pH 4.0 or 10.0 using either 12 M HCl or 10 M NaOH, respectively. The capillary was conditioned at each pH for 30 minutes (using 0.1 M NaOH for 10 minutes, distilled water for 10minutes, then the BGE for 10 minutes) prior to use in the CE-LIF analysis. Replicate runs of the CE system at each pH tested the reproducibility of the characteristic migration time of the dye, indicating the capillary’s stable condition.

In the preparation of nanoplastic standards for external calibration, a measured aliquot (1 µL) of a polystyrene nanospheres stock suspension was meticulously diluted using DDW (279 µL) inside a glass vial (2 mL capacity). The dilute nanoplastic suspension was subjected to manual agitation followed by ultrasonication in a water bath (for 2 minutes) to attain homogeneity. Concurrently, for the preparation of a working fluorescent dye solution, R6G dye (0.20 mg) was dissolved in the BGE solution (pH 5.0, 1.6 mL) inside another glass vial, culminating in a concentration of 125 µg/mL after ultrasonication in a water bath (for 2 minutes). Thereafter, the dilute nanoplastic suspension (commencing from 4 µL, increasing in steps of 4 µL, and culminating at 40 µL) was pipetted into the R6G solution (1.6 mL) in a glass vial. After each incremental addition, the mixture was subjected to manual agitation followedby ultrasonication in a water bath (for 2 minutes) to attain homogeneity. Prior to CE-LIF analysis, a baseline noise characterization of the instrumental system was performed. To reaffirm the reproducibility of the measurement results, each mixture was analyzed in triplicate.

Results and Discussion

The dyes that are useful for UV/LIF detection of nanoplastics would have to be unique and not easily found in nature or common industries, which is why R6G, DCM, disodium fluorescein and coumarins were chosen for the present study [44]. The first couple of fluorescent dyes tested were R6G.HCl (50%) and DCM (50%) in a rhody dye mixture. These two dyes were chosen as their fluorescence could be induced by the diode laser output wavelength of 480 nm and their emission could be detected through an optical interference filter with a narrow band-pass centered at 580 nm. R6G has an absorption maximum of 530 nm and an emission maximum of 556 nm due to the xanthene rings [45]. The DCM happens to be a charge-neutral molecule that was useful as a marker to indicate where neutral analyte peak appeared on the CE migration time scale. Although DCM absorbs maximally at 481 nm, it fluoresces the most at an orange wavelength of 644 nm due to the cyanine structure [46]. Conversely, R6G.HCl produces a R6G.H⁺ cation that was separated from DCM by CE, so no neutral dyes could interfere with its quantitative analysis. Several other dyes were also tested for better analytical sensitivity. They included coumarin 503, coumarin 540A, and disodium fluorescein, which all were capable of being excited by the 480-nm laser light. Disodium fluorescein was expected to emit a strong intensity of fluorescent light in the green portion of the visible spectrum at 531 nm. Although it has a stable xanthene ring structure similar to R6G, fluorescein is negatively charged [47]. Coumarin 540A was expected to be the best dye as its maximum absorption wavelength is 460 nm which matched the laser output wavelength of 480 nm very well [48]. Coumarin 503 was chosen as an alternative that might not be ideal because its maximum emission wavelength is 490 nm but it could still emit fluorescence in the blue-green region beyond 520 nm. This coumarin is a neutral compound due to the lack of charges on its molecules [49]. Both coumarin 503 and coumarin 540A are in a class of fluorescent dyes comprising the coumarin ring, which is an aromatic ring with a cyclic hydrocarbon chain impregnated with an ester and a single double bond.

Two detectors were used in the present CE study; they were a UV detector and an LIF detector as illustrated in Figure 1. The UV detector was reliable and consistent. Following the Beer’s law, UV absorbance was directly proportional to the dye concentration via its molar absorptivity in the wavelength range between 190 nm and 210 nm. Then mesityl oxide (0.1% by volume in methanol) was run to both test the electroosmotic flow (EOF) of BGE solution through the capillary and determine the migration time of all neutral molecules. Using 10 mM Na₂HPO₄ as the BGE solution at pH 9.4, the CE-UV peak for MO appeared at 4.20±0.03 min. Next, the rhody dye mixture was analyzed and produced a strong CE peak followed by a weak peak with UV detection. With BGE at pH 8.0, CE-UV analysis produced three FATP peaks at 6.01, 16.37 and 24.04 min and one MO peak at 5.60±0.07 min as expected from the use of a less alkaline pH.

Figure 1: Capillary electrophoresis setup with UV light absorption and laser-induced fluorescence emission detectors. Light shields to stop the laser beam and block room light are not shown for clarity.

The rhody dye mixture was used to analyze an aqueous sample containing polystyrene nanospheres (1.3 mg/mL) by measuring the strong CE-UV peak after each standard addition. The resultant peak height, corrected for a dilution factor and expressed in milli-absorbance units (mAU), was plotted against the spiked volume of rhody dye. As it can be seen in Figure 2(a), depicting the spiking of diluted polystyrene nanospheres with rhodamine dye for CE-UV analysis, revealed that the standard calibration curve demonstrated linearity prior to reaching saturation of the UV detector’s signal output. Most notably, extrapolation of the trend line in Figure 2(b) backwards intersected the x-axis at an intercept value (approximately 0.01 µg/mL), suggesting an initial dye concentration. This detection implies the substantial binding between the dye molecules and the polystyrene nanospheres, causing the apparent dye concentration in solution to diminish. A limitation in utilizing UV detection is the possible interference from uncharacterized components in the water matrix that absorb UV light at the same wavelength of 200 nm. Such components could co-migrate with the rhodamine dye during CE analysis, leading to confounded results. This interference underscores the need for careful control of matrix effects, particularly when assessing trace-level nanoplastic contamination.

Figure 2: Spiking diluted polystyrene nanospheres with rhody dye for CE-UV analysis: (a) high dye concentrations, and (b) low dye concentrations. BGE solution: 10 mM Na₂HPO₄ at pH 9.4; UV detection wavelength: 200 nm.

To establish a baseline signal and gauge potential interferences, the intrinsic fluorescence of the dye was first characterized in the absence of polystyrene nanospheres. This control measurement enabled the determination of the dye’s peak height, providing a reference for comparison once polystyrene nanospheres were introduced. By doing so, any changes attributable to the interactions between the dye and the nanoplastics could be accurately quantified. Furthermore, the incorporation of replicate blank samples, devoid of both dye and nanoplastics, facilitated the assessment of background noise and matrix effects on UV detection at 200 nm. These measures ensured that the subsequent analysis of nanoplastic-dye interactions was robust against potential confounding signals.

Alternatively, LIF was potentially more sensitive by about 100 times using an avalanche photosensor to measure the emission intensity. Both detectors determined an unknown concentration of the dye using standard solutions with known concentrations to construct a calibration curve. Disodium fluorescein dissolved fully in water/methanol (10:8 v/v) and produced a CE-LIF peak at the migration time of 4.96±0.48 min. However, DF had a flaw of contaminating the capillary inlet and hence the BGE solution, raising the baseline fluorescence significantly after several runs. Coumarin 503 and coumarin 540A were not fully dissolved in water/methanol (10:8 v/v). Their light blue cyan and green emissions required a different interference filter for optimal LIF detection. Therefore, the rhody dye was better for the CE-LIF setup as they had good solubility, migration times, and peak heights. Using a BGE solution at pH 9.4, the R6G and DCM peaks were observed at 3.53 min and 3.66 min respectively in Figure 3. Adding the LIF detector caused no noticeable harm to the original CE-UV system, requiring only the 480-nm laser beam to create a clear window on the same capillary for LIF detection. The capillary proved itself to be capable of generating repeatable results within the day, separating charged dyes of different characteristic migration times.

Figure 3: CE-LIF analysis of rhody dye. BGE solution: 10 mM Na₂HPO₄ at pH 9.4; applied voltage on diode laser: 5.0 V; λ_ex: 480 nm; photosensor reverse bias: 60 V; λ_em: 580 nm.

Next, the CE-LIF method was combined with frontal analysis to minimalize experimental errors due to a reduction in the manipulation of samples. Improved reproducibility was evidenced by replicate analysis of the rhody dye at different concentrations to construct the standard calibration curve shown in Figure 4. The electric charges of nanoplastics can significantly impact their physiochemical properties, solubility, electrophoretic mobility, reactivity, and binding interactions with other substances in water [50]. Their charge state depends on a variety of factors like the type of plastic material, the pH of water, and any surface coatings or modifications on the nanoplastics. Certain plastic materials can have intrinsic polarity when immersed in water. For example, polyethylene nanoparticles are generally neutral in charge but become negatively charged above pH 2.5 after surface oxidation [51]. Certain types of nanoplastics acquire a negative charge due to the presence of specific functional groups that ionize in water, e.g., polyacrylic/methacrylic acids (containing -COOH groups), polystyrene sulfonate (containing -SO₃H groups), and polyethylene terephthalate (its surface can be converted into -COOH and -OH groups). Negatively charged plastics attract positively charged toxins in the environment, leading to potential health hazards if consumed by local organisms [52]. Other types of plastics have a natural propensity to become positively charged when immersed in water, like poly diallyl dimethyl ammonium chloride (with quaternary ammonium groups), poly-4-vinylpyridine, and polyethyleneimine [53]. The surface of nanoplastics may be coated or modified to create cationic nanoplastics. For example, cationic polystyrene nanoparticles can be produced by incorporating positively charged groups (such as -NH₃⁺) on the surface [54]. Consequently, any changes of ionic charge affect the interaction of nanoplastics with water contaminants.

Figure 4: Combination of CE-LIF with frontal analysis to construct a standard calibration curve for rhody dye at different concentrations. BGE solution: 10 mM Na₂HPO₄ at pH 9.4; applied voltage on diode laser: 5.0 V; λ_ex: 480 nm; photosensor reverse bias: 60 V; λ_em: 580 nm.

Experimentally, using CE-LIF in the conventional analysis mode, both the % binding and the amount of rhody dye bound with 2.8 mg of polystyrene nanoparticles were determined. As presented in Figure 5(a), nearly 100% quantitative binding onto the nanoparticle surfaces was achieved at the lowest concentrations of rhody dye studied. This adsorption capacity of the nanoplastics was commensurate with their small particle size which has a direct correlation to large surface area, providing more sites for dye adsorption. Conversely, as shown in Figure 5(b), the amount of dye bound seemed to be reaching a saturation level with the highest dye concentrations studied.

Figure 5: Effect of rhody dye concentration on (a) % binding and (b) amount of dye bound, with 2.8 mg of polystyrene nanospheres.

The Langmuir adsorption model was attempted by fitting the above binding data with this linear isotherm equation C_e/q_e= C_e/q_max+ 1/K_Lq_max for c_e from 2.7 to 16 mg/mL, as demonstrated in Figure 6(a). The maximum adsorption capacity (q_max) for a saturated surface was determined from the reciprocal of slope to be 30 mg/g of nanospheres, and the half-saturation coefficient or Langmuir equilibrium constant (K_L) was then calculated from the y-intercept to be 4.8 mL/mg of rhody dye. The same binding data was next fitted with another isotherm equation 1/q_e = 1/q_max + 1/q_maxK_Lc_e for cross-checking purposes. As shown in Figure 6(b), q_max was determined from the reciprocal of y-intercept to be 27 mg/g of nanospheres, and K_L was calculated from the quotient of intercept and slope to be 2.8 mL/mg. Although the two q_max results were similar within model fitting errors, the two K_L results were rather discrepant mainly due to a larger statistical weight being put on the low c_e data points that skewed the slope. New results are reporting that the adsorption equilibrium constant of triclosan in a suspension of pristine polystyrene nanoparticles (100 nm) is 2.78 L/g [55]. Ion strength greatly affects the outer sphere complexation due to compression of the double electrical layer on each particle surface. The q_max value of a nanoplastic surface is hypothetically influenced by two crucial factors: chemical composition and nanoporous structures. Clearly, the R-square values of 0.8655 and 0.9171 suggested that the Langmuir adsorption isotherm might not be the best model for the binding of rhody dye on polystyrene nanospheres. The Langmuir model assumes that the adsorbate (rhody dye) molecules bind with a homogeneous surface of the adsorbent (polystyrene nanosphere) to form a monolayer without any interaction between the adsorbed molecules. It implies that the energy of adsorption on a homogeneous surface is independent of surface coverage, which may not be true. The nonlinear curves in Figures 6(a) and 6(b) however indicated that the Langmuir model was far from ideal for describing the binding of rhody dye with polystyrene nanospheres.

Figure 6: Langmuir isotherm models of rhody dye binding with 2.8 mg of polystyrene nanospheres at room temperature (23 ± 1°C): (a) c_e/q_e= c_e/q_max +1/K_L + q_maxand (b) 1/q_e = 1/q_max + 1/q_maxK_Lc_e.

The Freundlich adsorption isotherm, q_e = K_fc_e^1/n, is another equation widely used for data fitting to model the relationship between the sorbed mass (q_e) on a heterogeneous surface per unit weight of adsorbent and the aqueous concentration (c_e) at equilibrium [56]. Although the Freundlich equation is purely empirical, it provided important information regarding adsorption of rhody dye on the polystyrene nanospheres. The Freundlich isotherm plot in Figure 7(a) shows linearity from 2.7 up to 40 mg/mL (approximately 50% of maximum saturation), above which it became nonlinear. As shown in Figure 7(b), a linearized plot of log q_e = log K_f + 1/n log c_e, yielded an equilibrium partition coefficient K_f = 11 mg/g and a Freundlich exponential coefficient n = 2.0 through the y-intercept and slope respectively. K_f is a comparative measure of the adsorption capacity for the adsorbent, and it indicates the Freundlich adsorption capacity. Then q_max was calculated from n times K_f to be 22 mg/g at room temperature (23 ± 1^oC). The empirical constant n is related to the heterogeneity of the adsorbent surface [57]. For a favourable adsorption, 0 < n < 1, while n > 1 represents an unfavourable adsorption, and n = 1 indicates a linear adsorption [58]. A larger n value means that the system is more heterogeneous, which usually results in non-linearity of the adsorption isotherm [59]. A Freundlich exponential coefficient (n) in the range from 0.71 to 1.15 is newly reported for the adsorption of triclosan on pristine polystyrene nanoparticles.

Figure 7: Freundlich isotherm models of rhody dye binding with 2.8 mg of polystyrene nanospheres at room temperature (23 ± 1°C): (a) q_e = K_f c_e^1/n, and (b) log q_e = log K_f + 1/n log c_e.

The CE-LIF method was combined with electrokinetic sample injection to achieve rapid analysis of rhodamine B dye (migration time = 2.52 ± 0.01 min) at different concentrations to construct the standard calibration curve shown in Figure 8. A linear relationship is evident between the CE-LIF peak height for rhodamine B and the dye concentration up to 500 μg/mL, thanks to the short optical pathlength for laser excitation inside the fused silica capillary with an inner diameter of only 100 μm. Apparently, 400 μg/mL would be an optimal dye concentration of spiking with diluted polystyrene to repeat the CE-LIF analysis. Note that the peak height for each concentration was not maximal because the interference filter transmitted the fluorescence emission light most efficiently at a wavelength of 580 ± 5 nm which was rather different from the maximum emission wavelength of 645-650 nm for rhodamine B (in 10 mM BGE at pH 9.0), as illustrated in Figure 9(a). The same interference filter was a good match for DCM that exhibited a maximum emission wavelength of 550-570 nm in Figure 9(b).

Figure 8: Combination of CE-LIF with electrokinetic sample injection to construct a standard calibration curve for rhodamine B dye at different concentrations. BGE solution: 10 mM Na₂HPO₄ at pH 9.4; applied voltage on diode laser: 10 V; λ_ex: 480 nm; photosensor reverse-bias: 60 V; λ_em: 580 nm.

Figure 9: Fluorescence emission spectra obtained using Fluoromax-4 with λ_ex of 480 ± 5 nm from (a) rhodamine B dye, and (b) DCM, in 10 mM Na₂HPO₄ at pH 9.5.

Environmental conditions like pH, salinity, and temperature could influence the degree of dye adsorption onto nanoplastics. The effect of pH on the electrophoretic migration of rhodamine B was studied next. Figure 10 shows the normal trend of a longer migration time with a lower pH, as expected from a decrease in electroosmotic flow of the BGE solution. Note that two data points are presented for pH 6 based on duplicate measurements. The trend also indicated that each pH was ready for CE-LIF analysis after conditioning the capillary for 30 min. Sensitivity of the CE-LIF analysis, in terms of % binding, could be maximized after binding tests were conducted over a range of pH levels to determine an optimal pH based on Figure 11. A high result of 77% was obtained at pH 4.0 for the binding of rhodamine B with polystyrene nanospheres. This effect can be explained by the pK_a of 3.2 for rhodamine B; [60] and pH 9.9 for zero charge on polystyrene nanoplastics [61]. As the pH approached 4.0, the zeta potential of nanospheres became stabilized at +50 mV. The effect of pH on the ionization of rhodamine B had previously been reported [62]. There was apparently a stronger interaction between rhodamine B and polystyrene nanospheres at a lower pH. However, pH 5 was a better choice than pH 4 for the CE-LIF determination of nanospheres because the % binding of rhodamine B had a smaller standard deviation and the migration time of 7.7 min was shorter for each sample analysis.

Figure 10: Effect of pH on migration time of rhodamine B dye. Error bars indicate one standard deviation of uncertainty observed at each pH.

Figure 11: Effect of pH on % binding of rhodamine B dye with 2.8 mg of polystyrene nanospheres.

Using the BGE solution at pH 5 to condition the capillary, rhodamine 6G standard solutions were analyzed by CE-LIF to construct the calibration curve shown in Figure 12. The linear dynamic range can be seen to go from near zero up to approximately 150 mg/mL, in which the rhodamine 6G peak appeared at a migration time of 7.8 ± 0.2 min. This migration time became 7.9 ± 0.4 min when the full concentration range was studied up to 400 mg/mL. Compared to the migration time of 7.7 ± 0.3 min obtained in Figure 10 for rhodamine B at pH 5, these two dyes are too similar in their electrophoretic mobilities (despite their different molecular structures) to be separable by the present CE analysis method. Hence, the need for other fluorescent dyes that can be resolved as distinct peaks (with different migration times) remained.

Figure 12: Standard calibration curve for CE-LIF analysis of rhodamine 6G standard solutions. BGE solution: 10 mM Na₂HPO₄ at pH 5.0; electrokinetic sample injection: 6 s; applied voltage on diode laser: 8 V; λ_ex: 480 nm; photosensor reverse bias: 60 V; λ_em: 580 nm.

To validate the CE-LIF method, a constant concentration of the fluorescent dye R6G was analyzed across varying quantities of nanospheres in a series of water samples. In Figure 13a, there is a demonstrated positive correlation between the % binding of R6G and the mass of nanospheres ranging from 0.11 to 0.45 µg. This increase in % binding can be attributed to the additional surface area provided by a greater mass of nanospheres, which presents more potential binding sites for R6G molecules. Conversely, as depicted in Figure 13b, incrementing the mass of nanospheres further (from 10 to 350 µg) inversely affects the % binding. This counterintuitive result is interpreted as the onset of nanoparticle aggregation when in high concentration within the 1.6 mL water sample [63]. Aggregation reduces the effective surface area available for R6G binding, since clusters of nanospheres offer fewer exposed binding sites compared to the same mass of dispersed nanoparticles. To ensure the reliability of quantification in samples with high nanoplastic concentrations, such as those from industrial wastewater, it is recommended to perform serial dilutions. This approach ensures that measurements fall within the linear dynamic range, exhibiting a proportional decrease in % binding, thus yielding accurate assessments (as shown in Figure 13a).

Figure 13: Standard calibration curve for CE-LIF analysis of polystyrene nanospheres in 1.6 mL of water: (a) below 1 µg, and (b) above 10 µg. Rhodamine 6G dye concentration: 125 µg/mL; BGE solution: 10 mM Na₂HPO₄ at pH 5.0; electrokinetic sample injection: 6 s; applied voltage on diode laser: 8 V; λ_ex: 480 nm; voltage setting on photodetector: 6.0; λ_em: 580 nm.

The percentage of binding (% binding) has emerged as a valuable parameter for the quantification of nanoplastics in aqueous environments. Organic dyes, owing to their high affinity for diverse types of polymers, can achieve significant levels of binding. This characteristic makes the % binding an appropriate metric for the analysis of environmental water samples, potentially revealing the prevalence and persistence of nanoplastic contaminants. In the context of aquatic ecosystems, % binding can yield insights into the duration that nanoplastics may persist and how readily they interact with organic molecules. Nonetheless, employing % binding as a determinant for nanoplastic content in water analysis comes with inherent constraints. Specifically, there is an underlying assumption that the binding of the fluorescent dye to nanoplastics reaches an equilibrium state, a condition representing a balance between the adsorbed dye on the nanoplastic surface and the dissolved unbound dye in the surrounding medium. The validity of this equilibrium assumption is critical and must be empirically established to ensure accurate quantification. The interaction dynamics between fluorescent dyes and nanoplastics are intricate and carry implications for environmental surveillance and the tracing of pollution sources. To navigate these complexities, systematic research is essential to unravel the nuances of dye-nanoplastic interactions thoroughly. In-depth exploration of these relationships not only contributes to a better understanding of nanoplastic pollution in water bodies such as those in Ontario but also aids in refining the methodologies used to evaluate and safeguard water quality.

Could nanoplastic pollution be monitored by LIF detection without CE separation, or simply conventional spectrofluorimetry using the Fluoromax-4? Such monitoring would be possible if (and only if) all bound dye molecules settled with the nanoplastics to the sample vial bottom or stopped their fluorescence due to quenching by the plastic surface. The fluorescence quenching of organic dyes bound to nanoplastics depends on several factors including the photophysical properties of fluorescent dye, the physicochemical properties of nanoplastics, and environmental conditions of water. Dyes that are sensitive to their immediate environment could undergo photo-induced electron transfer or non-radiative decay mechanisms, leading tofluorescence quenching when bound to nanoplastics. The morphology, size, and surface charge of nanoplastics could modulate the quenching. Nanoplastics with higher surface charge density may enhance quenching. Nevertheless, accurate monitoring was made easy by CE-LIF where any nanoplastics carrying bound dye molecules in the sample suspension would migrate through the capillary at a low mobility and appear as a weak broad peak at a different migration time on the electropherogram. This was the main reason why we coupled CE with LIF to develop an advanced method for the accurate determination of aqueous dye concentration (c_e) at binding equilibrium. No worries about any potential errors, due to either fluorescence emission from the bound dye molecules or optical attenuation by the polymer nanoparticles, could be an issue.

Conclusion

As a part of the global water/wastewater sector concerning with environmental regulations and standards, rigorous quantification and understanding of contaminants in water are critical. The present work demonstrates how a LIF detector can be built onto a pre-existing CE-UV instrument for the sensitive determination of nanoplastics via their selective binding with organic dyes. The LIF detector can readily be placed anywhere along the length of the capillary, together with a diode laser, interference filter, and avalanche photosensor, without damaging the CE instrument. The original UV detector allows versatile analysis of many aromatic compounds including dyes while the additional LIF detector offers selective analysis of fluorescent dyes without any potential interference by organic compounds of low molecular weight that are commonly found in the aquatic environment. The CE-UV/LIF method has shown a potential to analyze real-world water samples for their nanoplastic content. It is a relatively inexpensive method for water analysis in quality control, public health, and environmental research purposes. For further development in industrial applications, the LIF detector assembly could be miniaturized for use as a retrofittable module to equip any CE-UV instrument that is commonly available in commercial research labs. This CE-based method could be further validated by high-performance liquid chromatography with UV or fluorescence detection, which is commonly accessible, for the versatile monitoring and control of water quality. We envision a future method wherein multiple fluorescent dyes could be used to detect different nanoplastic materials in water. Our studies will focus on developing efficient sample pretreatment techniques for the detection of nanoplastics in various water matrices. Sample treatment by ultrasonic homogenization can prevent aggregation/agglomeration of nanoplastics, prior to water analysis for free/residual dyes by the CE-LIF method. The interaction of nanoplastics with different water constituents requires careful exploration. Chemical methods that control and adjust the surface charge of nanoplastics to achieve better binding with fluorescent dyes would be beneficial. These new binding affinity results would provide a large dataset (dye structures, nanoplastics, matrix interferences) to facilitate water treatment quality control and management. Along with artificial intelligence-machine learning (AI-ML), fluorescent dye-based chemosensors will be better designed for future applications of CE-UV/LIF as one of the next-generation sensing technologies. Nanoplastics in lake/ground/well/tap water samples will be analyzed after sedimentation sorting, microfluidic binding with molecular dyes, CE separation, LIF detection, and barcode chemoinformatics.

Acknowledgement

We would like to thank Olay Chen for his tremendous help with the repair of a data acquisition system.

Data Availability Statement

All data generated or analyzed during this study, as presented in this published article, will be made available to any readers upon request from the corresponding author.

Disclosure Statement

No potential conflict of interest was reported by the authors.

Funding

Financial support from NSERC Canada (grant number RGPIN-2018-05320) is gratefully acknowledged.

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Glucagon-like Peptide 1 Receptor Agonists, Heart Failure, and Critical Appraisal: How the STEP-HFpEF Trial Unmasks the Need for Improved Reporting of Blinding

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DOI: 10.31038/EDMJ.2024813

The history of medical science demonstrates the effects of randomness where chance unmasks nature’s secrets. Penicillin’s accidental discovery of a contaminated Petri dish led to a new paradigm in the landscape of illness where the primary cause of human mortality was no longer infectious diseases but rather chronic, non-communicable disease, namely cardiovascular disease (CVD) [1]. Diabetes mellitus is an independent risk factor associated with a 2-to-4-fold increase in CVD-related mortality, and thus researchers have sought to identify new efficacious treatments [2,3]. One potential modality was identified in the 1980s as a mediator of glucagon-like effects: increased insulin secretion in a glucose-dependent manner while simultaneously blocking gastric acid secretion and motility [4]. It was named glucagon-like peptide 1 (GLP1) and synthetic forms of receptor agonists (GLP1RA) were later studied in clinical trials for the treatment of diabetes mellitus type 2. Despite numerous FDA approvals for this class of drugs due to their impact on blood glucose control, the promise around GLP1RAs seems somewhat analogous to penicillin: a chance finding of improved CVD outcomes and weight loss for patients with obesity who were treated, first with diabetes but then even those without a diabetes diagnosis [5,6]. Beyond the excitement surrounding these drugs and their impact on patient outcomes for CVD, there also exists significant market pressure from the financial sector with a projected $1 trillion in revenue globally over the next 30 years related to GLP1Ras [7]. In such a climate, the voice of clinicians can help ensure new treatments are adopted through the lens of the quintuple aim of healthcare [8]. This is to ensure implementation occurs with the greatest fidelity equitably and could optimally function within the infrastructure of a healthcare system with limited resources. However, barriers in the standard reporting of data related more broadly to blinded randomized controlled trials (RCTs) impede clinicians’ ability to complete the appraisal process. The 2023 RCT titled STEP-HFpEF (Effect of Semaglutide 2.4 mg Once Weekly on Function and Symptoms in Subjects with Obesity-related Heart Failure with Preserved Ejection Fraction) demonstrated the possible benefit of GLP1RA in heart failure. The trial was funded by the manufacturer of the study drug, and included adults with a left ventricular ejection fraction greater than 45%, and a body mass index greater than 30 kg/m². It assessed a primary two-part endpoint of both numeric change in subjective scoring of the Kansas City Cardiomyopathy Questionnaire (KCCQ) score plus a percentage change in body weight over a 12-month time frame. The KCCQ is a validated questionnaire that assesses subjective data related to a patient’s symptoms with scores ranging from 0 to 100. Results showed those treated with semaglutide had an average decrease in KCCQ of 7.8 (16.7 with semaglutide versus 8.7) and a 10% decrease in body weight loss (13.3% versus 2.6%). The authors concluded the use of GLP1RA improved heart failure symptoms in the heart failure population though it had limitations given a small proportion of enrollees were of non-white ethnicity which such that it could limit the external validity of the results. However, STEP-HFpEF offers a key lesson related to the application of critical appraisal that clinicians and researchers alike can glean when first evaluating the internal validity of a trial. Clinicians must assess for the preservation of blinding in RCTs where this is performed. Unmasking, where the blinding process fails to be implemented appropriately for either patients or care staff, could compromise a study’s results via the entry of ascertainment bias [9,10]. In response to a letter to the editor for the STEP-HFpEF trial, the authors said, “38% of the responding placebo recipients believed they had received semaglutide” [11]. Worded another way, it is inferred that 62% of respondents guessed correctly in the placebo group. Unfortunately, no data was provided for the semaglutide arm. With such a large proportion of patients identifying their assigned arm, it may be reasonable to question whether the behaviors and expectations of participants were compromised. Did a similar percentage of participants in the treatment arm guess correctly given their achieved weight loss, and thus had a higher subjective rating in the KCCQ questionnaire? A conservative goal should be for less than 20% of participants to identify their assignment where the blinding process is preserved correctly. Given that possibly more than three times that threshold guessed correctly, even despite differences in secondary endpoints of the trial, clinicians would be wise to think critically about adding this study as evidence to expand the use GLP1RAs for the indication of heart failure.

Though blinding is essential to the internal validity of a trial, STEP-HFpEF reveals a shortcoming of the status quo regarding information dissemination within the research community for blinded RCTs. The manuscript and supplement do not report data or blinding indices related to the evaluation of the blinding process despite the investigators having at least assessed for this in the placebo group based on their response. For greater transparency, publishers of blinded RCTs would benefit from making the assessment and reporting of blinding in both intervention and control arms a standard practice. In fairness to the authors, since it is not standard to have such data provided as part of the peer-review process, it is reasonable to have foregone this step at present. However, my question is, “should we though?”. Adding such reporting is imperative given the potential of ascertainment bias to inaccurately inflate efficacy outcomes, Objective demonstration of a trial’s internal validity will more readily ensure high-value practices are appropriately adopted [12]. Going forward, let us be thoughtful and transparent in the assessment of efficacy for new practices, ensuring the innovations of today achieve their desired outcome tomorrow in improving human health based on sound evidence versus adopting low-value practices based on noise. When nature reveals its secrets at random, the onus is on us to determine how best to apply that new knowledge.

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Glucagon and Beyond: Future Perspectives in Childhood

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DOI: 10.31038/EDMJ.2024812

Abstract

In a century of research, it has gradually become clear that glucagon should no longer be considered only as a counter-regulatory hormone of insulin accordingly to its role in the physiopathogenesis of metabolic pathologies such as diabetes, obesity and fatty liver appears to be decisive. As hyperglucagonemia represents the common feature of various metabolic pathologies not only in adults but also in pediatric patients, glucagon can be a problem but also a solution in the field of metabolic diseases. In fact, opposing therapeutic strategies have been developed which inhibit or enhance the activity of glucagon depending on the clinical situation and are also applied in pediatric age. This review aims to take stock of the situation on the physiopatogenetic role of glucagon in metabolic pathologies and bring together the dots of recent discoveries leading to the hypothesis of new solutions in the management and prevention ofthesepathologies.

Keywords

Glucagon, NAFLD, Obesity, Diabetes, Children

Introduction

In 2023 we celebrated the centenary of the discovery of glucagon, which occurred almost by chance since it was initially isolated as a contaminant of the first insulin preparations in 1923. However, the hormonal role of glucagon was only established in the 1950s. Recently, animal and human studies have confirmed the essential role of glucagon in glucose metabolism but have suggested equal importance for amino acid and lipid metabolism [1]. As considered an anti-insulin hormone, it was early on used to treat insulin-induced hypoglycemic coma episodes in people with Type 1 Diabetes Mellitus (T1DM). Nevertheless, a key step in the history of glucagon has been the discovery of its role and the role of α-cells in the physiology and pathophysiology of Type 2 diabetes (T2DM) and obesity [2]. In the last decades, research on glucagon has been slowed down by the difficulty encountered in carrying out glucagonemia measurements [3] which seems to have been overcome thanks to the development of a new high-quality ELISA method [4]. Currently, a century after the discovery of glucagon, there is still a lot to learn about this second pancreatic hormone and it seems necessary to re-elaborate the discoveries achieved so far to lay the foundations for innovative research projects.

Necessary Physiology Hints

Glucagon was initially known to be antagonist to insulin for its opposite metabolic effects on glucose metabolism. In particular, glucagon acts directly on glucose metabolism through three main mechanisms: in the liver, glucagon increases glucose production by stimulating glycogenolysis and gluconeogenesis [5] while in adipose tissue, glucagon stimulates lipolysis with the release of fatty acids and subsequent formation of ketone bodies in the liver [6] resulting both in a net increase of blood glucose levels; in contrast, glucagon acts on β-cells by inhibiting insulin production, thereby, giving a major contribute in maintaining glucose homeostasis. Therefore, glucagon binds specifically to Glucagon Receptor (GCGR), detected mainly in b-cells, liver cells and adipocytes [7]. However, the glucagon receptor has a wide distribution in the body and this explains its multiple known and potential effects. In fact, GCGR is also found in kidneys, heart, lymphoblasts, spleen, brain, adrenal glands, retina, and gastrointestinal tract [8]. Glucagon also controls indirectly blood sugar levels in the kidney through renal excretion by increasing water reabsorption and glomerular filtration and thereby glucose reabsorbtion [9]. Nevertheless, it is currently known that the role of glucagon is not limited to maintaining glucose homeostasis. In fact, glucagon appears to be the basis of a physiological response of satiety induced by meal, as glucagon concentrations increase during the consumption of a mixed meal [10]. The regulatory mechanisms that control glucagon-induced satiety are poorly understood but mediation of vagal afferent fibers in the hepatic branch that transmit signals to the central nervous system is hypothesized [5]. Furthermore, Glucagon promotes weight loss having a direct effect on slowing gastric emptying and increasing energy expenditure [11]. The mechanism of action of glucagon in the remaining areas of the body where its receptor is represented, such as retina, heart and gatrointestinal tract, still remains to be clarified.

Glucagon and Liver-α cell Axis

The main end organ for glucagon is the liver where a feedback axis, the “liver-alpha cell axis” (Figure 1), has been established [12]. In fact, the net increase in hepatic plasma glucose secretion, due to glucagon induced glycogenolysis and gluconeogenesis, determines a direct inhibition of glucagon secretion from α-cells. Furthermore, glucagon increases hepatic absorption and turnover of amino acids, leading to decreased aminoacids levels and inducing, thereby, ureagenesis, which again reduces the secretion of glucagon. Also, glucagon increases hepatic β-oxidation and decreases lipogenesis, lowering the circulating concentration of free fatty acids (FFAs). Although, a plausible mechanism through which lower circulating FFAs may inhibit glucagon secretion has not yet been established [6].

Figure 1: The liver-α-cell-axis in health. Modified from American Diabetes Association [The Liver-α-Cell Axis in Health and in Disease, American Diabetes Association, 2022]. Copyright and all rights reserved. Material from this publication has been used with the permission of American Diabetes Association.

Hyperglucagonemia: The Main Character

Metabolic disorders have long been thought to be caused by total or relative insulin deficiency: this is known as Insulin-centric theory [13]. However, in 1978 Unger and collaborators, in contrast to the insulinocentric theory and in light of discovery of the effects of glucagon, proposed the theory of bihormonal regulation [14]. They found that some metabolic disturbances associated with diabetes, such as elevated lipolysis, increased proteolysis, and impaired glucose utilization are directly caused by insulin deficiency; while others, such as decreased glycogensynthesis, increased ketogenesis, elevated liver glycogenolysis, and gluconeogenesis, are direct effects of excess glucagon. Lately, between the end of the twentieth century and the beginning of the twentyfirst century, the glucagonocentric theory was established, already intuited by Unger and his collaborators, supported by the following evidence: in mice lacking GCGR, insulin deficiency does not cause hyperglicemia, in humans hyperglucagonemia has been established in all forms of diabetes, therefore, excess glucagon represents the sine qua non for the development of hyperglycemia [15]. Physiologically, hypoglycemia represents the main stimulus to glucagon secretion. However, in individuals with diabetes, therefore in conditions of hyperglycemia, there is a paradoxical increase in glucagon in conditions of hyperglycemia. Until recently, this dynamic, which leads to hyperglucagonemia, was explained exclusively through the tonic inhibition exerted by insulin on α-cells, in light of the concept of unidirectional flow from beta to alpha cells [16]. Until the 2000s, it was, therefore, thought that the impact of alpha cells on β cell function was negligible, probably because the studies were mostly based on rodent islets in which α-cells are less represented than in humans [17]. Eventually, in the new millennium, a more sophisticated model of intra-islets vascular system with bidirectional flow and circulation integrated with the exocrine pancreas, was recognized. Therefore, an active role of α-cells has been recognized from both a physiological and pathophysiological point of view, leading to the concept of cross-talk between alpha- and beta-cells [18].

The Role of the Inter-cellular Cross-talk

Glucagon and insulin receptors are expressed on both alpha- and beta-cells, proving that there is a reciprocal relationship between them. Insulin exerts a tonic inhibition on glucagon production by α-cells directly through insulin receptor, therefore, a decrease in insulin induces increased glucagon production [19]. As GCGRs are more abundant in β-cells than Insulin Receptors in α-cells, it has been demonstrated that glucagon secretion acts a direct effect on insulin release [20]. Moreover, in condition of hyperglycemia, β-cells in close contact with the alpha cells release more insulin compared with β-cells deprived of these contacts [21]. It has also been shown that people with T2DM show elevated α-cell-to-β-cell mass ratios, potentially because α-cells are necessary for mantaining β-cell insulin secretion [22]. Although the action on GCGR, glucagon seems to stimulate insulin secretion predominantly via the GLP1 (glucagon-like peptide 1) receptor expressed on β-cell surface [23].

Hyperglucagonemia: The Common Feature

It is known that T1DM and T2DM recognize a different pathogenesis, but these two pathologies have in common hyperglucagonemia whose pathogenetic role has long been overlooked. Lack of postprandial suppression and subsequent glucagon hypersecretion is characteristic in patients with T1DM or T2DM [24]. Even individuals with subtle glucose metabolism disturbances without having clear diabetes mellitus may have excess glucagon in response to the OGTT [25]. Different causes of hyperglucagonemia can be hypothesized and, although it seems difficult to make a clear distinction between metabolic pathologies, since some of them constitute a continuum, recognizing the predominant mechanism in each of them could guide the therapeutic choice and determine a better efficacy, as summarized in the Table 1.

Table 1: Different causes of hyperglucagonemia

Main causes of hyperglucagonemia	Metabolic pathologies
1) Lack of suppression from insulin deficit	T1DM
2) Role of incretins	T2DM – OBESITY
3) Liver glucagon receptor resistance	T2DM – OBESITY
4) altered liver-alpha cell axis	NAFLD

Hyperglucagonemia in Obesity and NAFLD

It is known that Nonalcoholic Fatty Liver Disease (NAFLD) represents the most common chronic liver disease in children and adolescents and represents an early risk factor for the development of obesity and T2DM [26]. Studies revealed that hyperglucagonemia is more closely related to obesity and fatty liver disease than to diabetes: fasting hyperglucagonemia also occurs in individuals with obesity and normal glucose tolerance [27]. The proposed hypothesis is that NAFLD drives hepatic resistance to glucagon by altering the liver-alpha cell feedback mechanism (Figure 2) and thus resulting in increased circulating levels of aminoacids that stimulate α-cells to secrete glucagon resulting in hyperglucagonemia [28]. In fact, a study conducted in 2020 showed greater glucagon resistance at the level of liver aminoacid turnover in individuals with obesity and NAFLD compared to healthy lean (non-steatotic) individuals [29]. Given its causal role in hyperglucagonemia, plasma glucagon concentration could also be useful for identifying pediatric patients most at risk for NAFLD [30].

Figure 2: The liver-α-cell-axis in disease. Modified from American Diabetes Association [The Liver-α-Cell Axis in Health and in Disease, American Diabetes Association, 2022]. Copyright and all rights reserved. Material from this publication has been used with the permission of American Diabetes Association.

Hyperglucagonemia in Obesity and T2DM

In metabolic disorders such as T2DM and obesity the alteration of incretin production seems to prevail as possible mechanism responsible for hyperglucagonemia. About that, a study was conducted on patients aged 10 to 18 years with obesity and varying glucose tolerance from Impaired Glucose Tolerance (IGT) up to T2DM compared to controls with normal glucose tolerance. The authors demonstrated that, compared to controls, obese patients with impaired glucose tolerance exhibit a reduction in GLP1 levels in parallel with the increase in postprandial glucagon levels while an increase in fasting glucagon levels in parallel with a reduction in fasting GLP-1 levels [31]. These differences became more evident the more glucose tolerance was reduced. Therefore, an important role must also be recognized in the alteration of incretin levels. In light of this, it seems reasonable to deduce that T2DM therapy with GLP1 has a stronger rationale rather than metformin. Furthermore, a chronic hyperglycemic condition has been shown to increase the expression of the GCGR on the liver and decrease its downstream signaling. This means that a real mechanism of hepatic receptor resistance to glucagon is established [32]. Additionally, it is also hypothesized that the pathophysiology of T2DM is based on a mutation in the gene that codes for the GCGR [33,34].

Hyperglucagonemia and T1DM

In the light of what has been seen on the interaction between alpha- and beta-cells, in subjects affected by T1DM, insulin deficiency leads to the lack of tonic inhibition exerted by β-cell on α-cell, therefore, there is an increase in glucagonemia. Additionally, glucagon seems to play a crucial role especially evident in case of diabetic ketoacidosis (DKA) [35]. Thus, in insulin deficiency, glucagon prevails, FFAs are transferred from the circulation to the mitochondria of the liver cells. Then, the oxidation of FFAs takes place, and acetyl-CoA is produced and is used for the synthesis of ketone bodies [36]. However, there is a difference between the ketogenesis induced by physiological conditions such as fasting in order to find an alternative source of energy and the ketogenesis induced by pathological conditions like uncontrolled T1DM [37] in which it is the result of dysregulated metabolism and a lack of insulin and is not intended to function as an energy source [38]. It is widely known that DKA can cause several adverse events and multiply the risk of developing diabetic complications as ketones lead to increased oxidative stress and inflammation which affect mainly cardiomyocytes, erythrocytes, and endothelial cells [39]. Additionally, elevated plasma ketone concentrations appear to be involved in reducing cell surface insulin receptors, leading to increased Insulin Resistence [40]. Since during DKA glucagon production is increased and is responsible for harmful effects on the body exactly like insulin deficiency, it could probably be useful to intervene on hyperglucagonemia and not just manage hyperglycemia and insulin deficiency.

Therapeutic Perspectives in Metabolic Disorders

Hyperglucagonemia and α-cell hyperplasia drive and accelerate metabolic dysfunction [41]. However, studies indicate that, through intra-island paracrine communication, α-cells could enhance β-cell function and preserve them. In fact, increased secretion of glucagon in metabolic diseases is the result of an α-cell and possibly also gut-derived adaptation for the maintenance of energy balance in favor of the β cells [42]. Whether hyperglucagonemia in metabolic disease is a pathogenic responsible or represents a metabolically helpful adaptation remains unclear [43].

What is the appropriate therapeutic approach? In consideration of the fundamental role that glucagon plays in the pathogenesis of metabolic disorders, the main current therapies and those currently under study are based precisely on the management of glucagonemia. The best choice of type of therapy depends on the type of metabolic disorder and its stage.

Glucagon Antagonism

Hyperglycemia patients treated with insulin is driven, at least in part, by hyperglucagonemia and, therefore, contrastable by antagonization of glucagon secretion or action [44]. GCGR antagonism has been proposed as a pharmacological approach for the treatment of T1DM or T2DM, and it is possible through receptor antagonists, monoclonal antibodies (mAbs) against GCGR and antisense oligonucleotides that reduce receptor expression [45]. GCGR mAbs can also induce b-cell regeneration through the trans-differentiation of a portion of pancreatic α-cells or δ-cells into β-cells [46]. A single dose of REMD-477 (Volagidemab) significantly reduces insulin requirement in patients with T1D improving glycemic control without serious adverse reactions [47]. Data are limited and require further study.

The Multi-effectiveness of GLP-1 Analogues

Last but not least Glp-1 analogues (GLP1A) are now well-known and widely used drugs for the treatment of obesity, but they seem even more effective than insulin and metformin in the management of T2DM and could find application as an additional therapy also in T1DM.

The strength of the GLP1A is represented by its pleiotropy: enhances glucose-dependent insulin secretion; inhibits glucagon secretion; promotes the survival, growth and regeneration of pancreatic β-cells; slows gastric emptying and reduces food intake (GLP1A also find application in the pharmacological therapy of pediatric obesity)[48].

It is reasonable to assume that even with GCGR mutations in β-cells, the binding of glucagon to GLP-1R is conserved, therefore, GLP-1A overcome the limits of GCGR antagonism too.

GLP-1A in T2DM

Currently, first-line therapies for the treatment of T2DM in children over 10 years of age and adolescents, in addition to diet and exercise, include insulin and metformin while GLP-1A as a second line. Nowdays the incidence of juvenile-onset diabetes (JOD) is increasing accordingly the increasing prevalence of obesity in adolescents [49] and it must be considered that, compared with adult-onset T2DM, JOD is associated to: more severe impairment of pancreatic B-cell function, which is further complicated by the increase in insulin resistance associated with obesity and puberty; higher rates of microvascular and macrovascular complications, despite a shorter disease duration than in other types of diabetes; higher treatment failure rate of metformin, which is used as a first-line drug for type 2 diabetes [50]. Therefore, there will likely be an increasing use of GLP-1A prior to the initiation of insulin given their potential benefits on weight and glycemic control but especially the antagonistic action of glucagon. In fact, a study showed that weekly treatment with Dulaglutide was superior to placebo in improving glycemic control over 26 weeks among young people with type 2 diabetes treated with metformin and/or insulin [51].

GLP-1A in T1DM

In T1DM, residual β-cell function is minimal, if not completely absent. Therefore, GLP-1A cannot have any effect on the stimulation of insulin secretion in these subjects. In addition to glycemic control which represents the target of insulin therapy, two other non-negligible aspects in the management of T1DM concern weight gain and the paradoxical increase in glucagon refractory to the action of the administered insulin [52]. A study demonstrated better glycemic control, weight reduction, a lower insulin daily dose and especially a significant reduction in total and postprandial glucagon levels in patients with combined therapy insulin-GLP1A [53]. Moreover, other Authors showed that postprandial glucagon levels tend to progressively increase with the duration of T1DM and correlate positively with deterioration of glycemic control and loss of β-cell function [54]. If GLP-1 levels followed the rising trend of glucagon while GLP-1 is thought to negatively modulate glucagon secretion, there would be a difference between the action obtained from physiological levels of GLP-1 and pharmacological ones during therapy with GLP-1A. In light of these results, a new starting point can be defined for the rationale for the use of GLP-1A in association with insulin therapy. Also, in a recent trial, Liraglutide appears to exert an inhibitory effect on ketogenesis through glucagon reduction [55]. Furthermore, another work shows that Liraglutide, not only markedly suppresses the post-prandial excursion of glucagon in a dose-dependent manner, but it also suppresses fasting plasma FFAs concentrations, and therefore ketogenesis, in patients with T1DM [56].

New Challanges

Hyperglucagonemia represents a fundamental pre-requisite for the development of all forms of diabetes but also obesity, and it is due to insulin deficiency, glucagon receptor resistance, imbalance of incretin secretion, and impaired liver-alpha cell axis. Hepatic steatosis, present in almost all obese pediatric patients, could be the main responsible for the establishment of glucagon-resistance. Therefore, hyperglucagonemia could also be considered a valid marker for the development of metabolic diseases in pediatric patients, as useful tool in the prevention strategy. Whereas, the challenge in pharmacological research is to balance the beneficial effects of glucagon on body weight and lipid metabolism with its hyperglycemic effects. Therefore, dual- and tri-agonists combining glucagon with incretin hormones have been developed and studied as anti-diabetic and anti-obesity therapies [57,58]. The GIP/GLP-1-agonist Tirzepatide has been approved by FDA for the treatment of T2DM, and according to clinical studies, Tirzepatide proved to be more effective than Semaglutide also in reducing body weight in patients with obesity [59]. Finally, among the therapeutic perspectives, the real challenge is to approach metabolic pathologies by trying to broaden the targets of action. What if we were only treating part of diabetes by giving insulin and metformin? What if we also considered glucagon in the management of diabetic ketoacidosis? There are numerous questions still unanswered. Shifting the focus of therapy can represent a winning strategy in the management of metabolic pathologies and this is what we hope for, especially for the pediatric population.

Conflict of Interest Statement

The authors have no conflicts of interest to declare.

Funding Sources

This study was not supported by any sponsor or funder.

Author Contributions

Conceptualization, Writing and Editing – G.D.P. and A.M.;

Project administration and Supervision – F.C.;

All authors read and approved the final manuscript.

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Palliative Medicine Symptom Management for Geriatric Patients

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DOI: 10.31038/JPPR.2024712

Abstract

The landscape of palliative medicine, particularly concerning symptom management in older adults with serious illnesses, continues to evolve, necessitating periodic updates to clinical approaches and guidelines. This article provides a comprehensive exploration of the challenges, strategies involved in optimizing the quality of life for this vulnerable population, and a commentary of the “Symptom Management in the Older Adult: 2023 Update.

Introduction

Geriatric palliative medicine seeks to enhance the quality of life for older adults facing serious illnesses. It underscores the importance of viewing symptom management through a holistic lens, considering not only physical symptoms but also psychosocial and existential aspects. Frailty is highlighted as a significant factor influencing symptom management decisions, necessitating tailored approaches along the illness trajectory. Moreover, the impact of external factors such as the opioid epidemic and the COVID-19 pandemic underscores the dynamic nature of symptom management in this context.

Pain Management

Pain management in older adults with serious illnesses represents a multifaceted challenge requiring a nuanced and individualized approach. Chronic pain, a prevalent issue in this population, not only diminishes quality of life but also poses unique barriers to effective management. The “Symptom Management in the Older Adult: 2023 Update” delves into the complexities of pain assessment, pharmacologic interventions, and non-pharmacologic strategies tailored to the specific needs of older patients facing serious illnesses.

Assessment Challenges

Assessing pain in older adults presents unique challenges due to factors such as underreporting and atypical pain presentations. Older adults may attribute pain to aging or hesitate to report it, leading to underestimation of its prevalence and severity. Moreover, comorbidities and cognitive impairment can obscure pain assessment, as pain may manifest as behavioral changes rather than verbal expressions. We emphasize the importance of adopting a patient-centered approach, prioritizing the patient’s pain experience and preferences in the assessment process.

Pharmacologic Interventions

Pharmacologic interventions remain cornerstone modalities in pain management, but their use in older adults requires careful consideration of factors such as frailty, comorbidities, and medication interactions. Opioids, while effective for pain relief, are often underutilized due to concerns about side effects and addiction. We advocate for judicious opioid prescribing, starting at the lowest effective dose and titrating slowly while monitoring for adverse effects. It also emphasizes the importance of patient and caregiver education regarding opioid use, dispelling myths, and addressing concerns to optimize adherence and safety.

Adjuvant Agents and Non-Pharmacologic Strategies

In addition to opioids, adjuvant agents play a crucial role in pain management, particularly in older adults with complex medical profiles. Non-opioid analgesics such as acetaminophen and nonsteroidal anti-inflammatory drugs (NSAIDs) offer alternative options for mild to moderate pain, but their use requires careful monitoring for adverse effects, especially in older adults with comorbidities such as renal impairment or gastrointestinal bleeding risk. Beyond pharmacologic interventions, non-pharmacologic strategies play a pivotal role in holistic pain management approaches. We seek to highlight the importance of integrating non-pharmacologic modalities such as physical therapy, acupuncture, cognitive-behavioral therapy, and mindfulness-based interventions into pain management plans. These modalities not only complement pharmacologic treatments but also address psychosocial factors contributing to pain perception and coping mechanisms.

Individualized Care

Central to effective pain management in older adults is the principle of individualized care. Each patient’s pain experience is unique, influenced by factors such as cultural background, psychological resilience, and social support networks. The commentary advocates for a personalized approach that considers the patient’s goals, preferences, and values when formulating pain management plans. Shared decision-making between patients, caregivers, and healthcare providers ensures alignment with patient priorities while optimizing treatment outcomes.

Challenges and Opportunities

While significant progress has been made in pain management approaches for older adults with serious illnesses, challenges persist, necessitating ongoing research and innovation. Our review acknowledges the need for further studies to elucidate optimal pain management strategies tailored to the complex needs of older patients. Additionally, addressing barriers such as stigma surrounding opioid use and expanding access to multidisciplinary pain management services are crucial steps toward improving pain care delivery and outcomes in this vulnerable population. Pain management in older adults with serious illnesses requires a comprehensive, multidimensional approach that integrates pharmacologic and non-pharmacologic modalities while prioritizing patient-centered care. By addressing the unique challenges and opportunities inherent in pain assessment and treatment, healthcare providers can enhance the quality of life for older adults facing serious illnesses, mitigating the burden of pain and promoting overall well-being.

Fatigue

Fatigue emerges as a prevalent and distressing symptom in older adults with chronic diseases. Early recognition and intervention to mitigate its impact on patients’ well-being is important. While we acknowledge the limited evidence base for fatigue management in this population, we emphasize the need to explores potential pharmacologic and non-pharmacologic interventions tailored to individual patient needs.

Neurologic and Psychiatric Symptoms

Depression, anxiety, insomnia, and delirium represent significant challenges in the management of older adults with serious illnesses. There is a complex interplay between these symptoms and the importance of comprehensive assessment and management strategies needs to be of primary focus. There needs to be continued discussion regarding pharmacologic interventions while weighting the need for caution and individualization, particularly considering the older adult population’s unique characteristics and vulnerabilities.

Respiratory Symptoms

Dyspnea and cough are common respiratory symptoms that can significantly impact the quality of life for older adults with serious illnesses. There are various approaches to symptom management, including both pharmacologic and non-pharmacologic interventions. For some patients, there may be a role of opioids in managing dyspnea depending on individualized treatment plans tailored to the underlying etiology and patient preferences.

Gastrointestinal Symptoms

Constipation, nausea, vomiting, and cachexia/anorexia are prevalent gastrointestinal symptoms in older adults with serious illnesses. Extra attention should be placed towards obtaining an accurate diagnosis and creating individualized treatment approaches, considering factors such as comorbidities and medication interactions. It provides an overview of pharmacologic and non-pharmacologic interventions aimed at alleviating these distressing symptoms and improving patients’ overall well-being.

Miscellaneous Bothersome Symptoms

The commentary addresses additional bothersome symptoms such as itching and hiccups, highlighting potential causes and treatment options. It emphasizes the importance of a comprehensive approach to symptom management, considering both pharmacologic and non-pharmacologic interventions tailored to individual patient needs.

Summary

In summary, the “Symptom Management in the Older Adult: 2023 Update” provides a thorough examination of the complexities involved in optimizing the quality of life for older adults with serious illnesses. Through a multidimensional approach that considers physical, psychosocial, and existential aspects, the review offers insights into tailored symptom management strategies. However, it also acknowledges the limitations of the current evidence base and underscores the need for further research to enhance our understanding and improve outcomes in this population. Overall, the review serves as a valuable resource for clinicians navigating the intricacies of palliative care in older adults.

Revisiting MC Carbide Formation in a Ni-Co-Cr Alloy with Additions of Carbon and Distinct Transition Metal Elements W, Ta, Ti or Hf

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DOI: 10.31038/NAMS.2024723

Abstract

We revisit the formation of MC carbides in a model Ni-Co-Cr alloy containing carbon and the transition metal elements Hf, W, Ta, or Ti. We aim at illustrating that the thermodynamic stability of the MC carbides sensitively depends on the transition metal at case. All investigated alloys contain 1.15 at.% C and 1.20 at.% M (Hf, W, Ta, or Ti) being added to the baseline alloy Ni-31.3Co-31Cr (at.%). The selected carbon content is rather high and significantly higher compared to common superalloys, featuring carbide-reinforced alloys as potential candidates for laser based manufacturing. Thermodynamic computations using the Thermo-Calc software and the TCNi8 database show that only HfC is stable over the entire temperature range from room temperature up to the eutectic temperature of HfC formation, which is just above the solidus temperature of the Hf and C containing alloy.

Keywords

MC mono-carbides, Transition metal carbides, Thermodynamic computations, Carbide-reinforced alloys

Introduction

In most nickel-base alloys, transition metal mono-carbides (MC) with the crystal structure of NaCl [1] form in-situ during the late stages of solidification following a terminal eutectic reaction. In casting conditions, the MC carbides are therefore found in interdendritic regions only, quite often displaying the so-called Chinese script morphology [2-4]. These carbides are efficient to reduce grain boundary sliding during creep, but may deteriorate the tensile ductility and fatigue resistance by offering crack initiation sites and preferential crack propagation paths. Laser based processing, e.g. laser powder bed fusion, opens up new pathways for controlling the size and distribution of in-situ formed carbides. Recent articles have indeed reported that nano-sized and well-dispersed carbides are obtained by additive manufacturing in a number of Ni-based alloys [5-8], however the nature of the carbides is largely dependent on the alloy composition. This motivated us to revisit the thermodynamic stability of carbides in a model Ni-Co-Cr alloy containing carbon and the transition metal elements Hf, W, Ta, or Ti using thermodynamic computations with the software Thermo-Calc and the TCNi8 database [9]. In this short article, we will report on computed phase equilibria as function of temperature. In addition, we display the evolution of the solute content of Hf, W, Ta, or Ti in the solid solution matrix phase with the FCC face centered cubic crystal structure. We recall that phase equilibria are relevant for devising heat treatments and certainly for considerations of the phases in equilibrium with one another at the envisaged operation temperature. They are not sufficient for understanding phase evolution under the fast and directional solidification conditions inherent to laser processing.

Phase Equilibria and Thermodynamic Stability of MC Carbides

To address the question regarding the formation of carbides and the stability range of MC carbides we devised a generic alloy composition with carbon C and the transition metal M in nearly equimolar quantity (Table 1). Four alloys were defined accordingly; their composition in being listed in Table 1. For convenience we use wt.% instead of at.%.

Table 1: Overview of alloy compositions

Alloy composition	Ni	Co	Cr	C	M = (Hf, Ta, W, Ti)
Generic (M), at.%	35.35	31.3	31.0	1.15	1.20 M
Alloy 1 (Hf), wt.%	36.0	32.0	28.0	0.24	3.72 Hf
Alloy 2 (W), wt.%	36.0	32.0	28.0	0.24	3.83 W
Alloy 3 (Ta), wt.%	36.0	32.0	28.0	0.24	3.77 Ta
Alloy 4 (Ti), wt.%	37.0	32.9	28.8	0.25	1.03 Ti

The computed phase equilibria are shown in the left-hand diagrams (a) of Figures 1-4, displaying the volume fraction of equilibrium phases as function of temperature. In all cases the low fraction range was chosen spanning from 0.0 to 0.2, i.e. from 0 to 20 vol.% The right-hand diagrams, labelled (b), display the corresponding evolution of the solute content inside the solid solution FCC phase, focusing on the element M, either Hf, W, Ta or Ti, by case.

Alloy 1 containing M=Hf in an amount of 1.2 at.% (3.72 wt.%) forms HfC from the last solidifying liquid (Figure 1a). The eutectic reaction LiquidFCC+HfC+Liquid’ extends over a rather wide temperature interval, which calls for special attention when applying laser-based additive manufacturing. The HfC is outstandingly stable over the widest temperature range down to room temperature. Below 700°C a trace amount of Cr₂₃C₆ forms in addition, but not at the expense of HfC. The Hf content in the FCC solid solution (Figure 1b) is very limited with a maximum value at T@1200°C as low as 0.2 at.% and gradually decreasing with decreasing temperature.

Figure 1: Thermodynamic equilibria in Alloy 1 with M=Hf

Alloy 2 containing M=W in an amount of 1.2 at.% (3.83 wt.%) forms Cr₇C₃ carbides from the very last solidifying liquid (Figure2a). All tungsten remains dissolved in the FCC solid solution. Around T@900°C the Cr₇C₃carbide transforms to (Cr,W)₂₃C6 along with a modest decrease of the W content in the solid solution. The R-phase Co₂₇Cr₁₈W₈ stable below ~600°C finally consumes significant amounts of the dissolved W.

Figure 2: Thermodynamic equilibria in Alloy 1 with M=W

Alloys 3 and 4 with M=Ta and M=Ti, respectively, behave quite similar with MC and Cr₇C₃ carbides co-existing at high temperatures (Figures 3 and 4). A small difference relates to the carbide in the eutectic, being Cr₇C₃in alloy 3 and TiC in alloy 4. Accordingly, the maximum solute content of Ta in the FCC solid solution is higher than that of Ti. In both alloys the Cr₇C₃carbide transforms to (M,Cr)₂₃C₆ at around 820°C. The MC carbides remain stable down to about 680°C, where they dissolve in favor of the intermetallic compounds Ni₃Ta (Struktur-bericht designation D0a) or Ni₃Ti (Strukturbericht designation D0₂₄).

Figure 3: Thermodynamic equilibria in Alloy 3 with M=Ta

Figure 4: Thermodynamic equilibria in Alloy 4 with M=Ti

Comparing the alloys at case one must conclude that HfC is outstandingly stable from a thermodynamic viewpoint. In the absence of “parasitic” Cr₇C₃ carbides the Cr content of the FCC solid solution phase is also highest in Alloy 1, which may be relevant for the oxidation behavior. For convenience Table 2 lists the full composition of the FCC solid solution phase at T=1050°C, seen as a potential operation temperature.

Table 2: Calculated composition of the FCC solid solution matrix at T=1050°C

FCC composition	Ni	Co	Cr	C	M = (Hf, Ta, W, Ti)
Alloy 1 (Hf), wt.%	37.4	33.3	29.0	0.02	0.31 Hf
Alloy 2 (W), wt.%	36.8	32.7	26.7	0.05	3.83 W
Alloy 3 (Ta), wt.%	36.9	32.8	27.3	0.05	2.96 Ta
Alloy 4 (Ti), wt.%	37.6	33.4	28.5	0.05	0.48 Ti

Summary and Outlook

We revisited the formation of MC carbides in a model Ni-Co-Cr alloy containing carbon and the transition metal elements M being Hf, W, Ta, or Ti. A generic alloy composition was devised with carbon C and the transition metal M in nearly equimolar quantity, i.e. 1.15 at.% C and 1.20 at.% M. We used thermodynamic computations to reveal phase equilibria as function of temperature, thus helping to understand the sequence of phase formation and possible phase transitions. The results are summarized as follows:

(1) All alloys give rise to in-situ carbide formation, with distinct carbides solidifying from the last fraction of liquid in a so-called terminal eutectic reaction.

(2) Among the investigated transition metal elements M, hafnium is the only element to provide fully stable MC-type carbides and thus an alloy composed of FCC and HfC over the widest temperature range.

(3) For the nearly equimolar carbon and hafnium content, virtually all C and Hf are bonded in the HfC, leading to an FCC solid solution with low Hf and C content. In the absence of “parasitic” Cr₇C₃the Cr-content of the FCC solid solution is highest.

On this thermodynamic background, one may further proceed to design alloys for casting and laser-based additive manufacturing. Laser based processing, e.g. laser powder bed fusion, opens up new pathways for controlling the size and distribution of in-situ formed carbides, taking advantage of the fast and directional solidification inside travelling melt pools. First experiments with an alloy composition close to Alloy 1 were already performed by the authors and submitted to publication.

Acknowledgement

The authors would like to acknowledge P. Berthod and co-workers from the Université de Lorraine, Institut Jean Lamour, France for many fruitful discussions on carbide-reinforced alloys.

Competing Interest Statement

The authors have no competing interests to declare.

Data Availability Statement

To reproduce our figures the Software Thermo-Calc and the database TCNi8 are required.

References

Nakamura K, Yashima M (2008) Crystal structure of NaCl-type transition metal monocarbides MC (M=V, Ti, Nb, Ta, Hf, Zr), a neutron powder diffraction study, Materials Science and Engineering B, 148: 69-72.
Murata Y, Suga K, Yukawa N (1986) Effect of transition elements on the properties of MC carbides in IN-100 nickel-based superalloy, Journal of Materials Science 21: 3653-3660.
Berthod P (2009) High temperature properties of several chromium-containing Co-based alloys reinforced by different types of MC carbides (M = Ta, Nb, Hf and/or Zr), Journal of Alloys and Compounds 481: 746-754.
Conrath E, Berthod P (2018) Properties of a HfC-reinforced nickel-based superalloy in creep and oxidation at 1100 C, Materials Science 53: 861-867.
Wang R, Zhu G, Yang C, Zhou W, Wang D, et al. (2020) Novel selective laser melting processed in-situ TiC particle-reinforced Ni matrix composite with excellent processability and mechanical properties, Materials Science and Engineering A 797: 140145.
Xia T, Wang R, Bi Z, Wang R, Zhang P.et al. (2021) Microstructure and mechanical properties of carbides reinforced nickel matrix alloy prepared by selective laser melting, Materials 14: 4792.
Kim YK, Yu JH, Kim HS, Lee KA (2021) In-situ carbide-reinforced CoCrFeMnNi high-entropy alloy matrix nanocomposites manufactured by selective laser melting: Carbon content effects on microstructure, mechanical properties, and deformation mechanism, Composites Part B. Engineering 210: 108638.
Chen H, Lu T, Wang Y, Liu Y, Shi T, et al. (2022) Laser additive manufacturing of nano-TiC particles reinforced CoCrFeMnNi high-entropy alloy matrix composites with high strength and ductility, Materials Science and Engineering A 833: 142512.
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Meaning of Supercritical Fluids in Pegmatite Formation and Critical-Element Redistribution

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DOI: 10.31038/GEMS.2024625

Abstract

This contribution shows that the water for some pegmatites comes from mantle deeps via supercritical fluids. Proofs are silicate melt inclusions with high water concentration. This concentration forms with the temperature solvus curves. In addition to such solvus curves, the distributions of a row of elements display characteristic Lorentzian curves regularly related to the solvus. Along with these characteristics, untypical, mostly spherical indicator minerals from mantle deeps are present in the granites and associated pegmatites in the upper crust regions. Nanodiamonds, graphite, coesite, and reidite belong to such minerals.

Keywords

Pegmatites, Supercritical fluids, Mantle minerals, Extreme element enrichment

Introduction

In recent years, we have seen an increasing interest in the genesis of pegmatites. The origin of this is the importance of rare elements, like Li, Be, Rb, Cs, REE, Nb, Ta, and many others, which are sometimes highly enriched in some pegmatites. That is also demonstrated by regular meetings and field trips, as well as by books [1,2] or publications on the classification of granite pegmatites [3]. Mainly in the discussion of the origin of pegmatites, those of the alkaline massifs (e.g., Lovozero Alkaline Massif/Kola peninsula; see Pekov, 2000) [4] are primarily unconsidered. In the end, the formation of miarolitic and other pegmatites is still a problem. The origin of the large Volyn chamber pegmatites (20 x 20 x 15 m [2] or the smaller miarolitic pegmatites in many granites is not entirely solved. The crucial questions are the origin of the water and the extraordinary enrichment of rare elements, which require a strong pre-enrichment. However, the origin of the water and the room-building energy is unclear, too. A typical case is the famous Sauberg tin deposit near Ehrenfriedersdorf in Saxony. Schröcke [5] has, in his notable contribution to the paragenesis of the tin deposits of the Erzgebirge, shown that the number of pegmatite veins and bodies is so large that the derivation of the necessary water cannot be the adjoining granites. The question of the origin of water for the formation of pegmatites is already unanswered. There is tacit consent that the water comes from the granite itself. Some authors ignore water as an essential component (for example, London 2008). However, water is an indispensable compound for pegmatite formation. Lindgren’s [6] model is often used to explain the formation of the different stages, starting at the liquid magmatic stage, the pegmatite stage, the pneumatolytic stage, and the hydrothermal stage at the end. However, Niggli published such a diagram (page 120) and explanation (page 122) in 1920 [7]. Fermann [8] presents his geochemical-genetic classification of granite pegmatites, which is the basis of later works in this field. In his work (1931b) [9], Fersmann suggests in Figure 5 (page 676) a direct relationship of pegmatites, pneumatolites, and hydrothermalites to the granites and has anticipated the idea of Lindgren (1937) [6]. The present authors have a contradictory opinion. At least a part of the pegmatites results from the interaction of supercritical fluids from mantle deeps with the granites at the intrusion level. Since the beginning of studies on melt inclusion in pegmatite quartz from the tin deposits, particularly on the pegmatites from the Sauberg mine in the Ehrenfriedersdorf district (Central Erzgebirge, Germany), in the years around the turn of the century, the first author often found very water-rich melt inclusions. Water content and temperature appear to be a characteristic relationship from the beginning: a typical solvus curve [10,11]. After that, such curves were also found for many other pegmatites and evolved granites (Figure 1).

Figure 1: Pseudo-binary solvus curve for 19 different evolved granites and pegmatites worldwide. Note: each point represents the arithmetic mean of measurement on up to 100 melt inclusions [12].

Remarks to Figure 1: Values at T/T_C(T in °C) = 1.0 correspond to the solvus curve’s critical point (CP). The abscissa (analytically determined water content) first approximates the melt density. Note here that the point scattering is, in a first approximation, the result of the complex interaction of volatiles (H₂O, F, H₃BO₃), which sometimes work additively (according to Thomas and Rericha, 2023) [12]. Also, minor errors due to the inaccuracy of analytical measurements are additionally possible. For the origin of such curves, a clear answer could not given at this time. The necessary analytical technique was still in its infancy – however, the evidence of the characteristic relationship between water content and temperature increases significantly yearly. Such solvus curves were also obtained during hydrothermal diamond anvil cell (HDAC) experiments [13,14]. However, the indispensable step from experiment to nature is often missing. See to this Niggli (Figure 2a) [7].

Applying critical parameters (H₂O_crit, T_crit, and C_A-crit) displays relatively good comparability for different granite and pegmatite systems (Figure 2b). Figure 2b is a combination of Figures 1 and Figure 2a schematized.

Such Lorentzian curves are typical for many pegmatites, evolved granites, and other mineralizations, like the Habachtal emerald deposit [15-17]. Figure 2b schematically combines the solvus curves (water versus temperature) with the Lorentzian distributions of elements or anions, like SO₄^2- (Königshain), or CO₃^2- (Habachtal), or Rb, Cs, Be, Sn for the Ehrenfriedersdorf pegmatites and high-temperature mineralizations.

Figure 2a: Plots the normalized element concentration C_A/C_A-crit versus the normalized water concentration H₂O/H₂O-crit. This Lorentzian distribution is based on 216 data points (2-10 measurements of each point.

Figure 2b: Schematic three-dimensional diagram in the reduced coordinates (following Guggenheim, 1945 [18]; however, we do not use absolute temperatures here). X-axis: water concentration in a silicate melt (H₂O) divided by the water concentration at the critical point (CP). Y-axis: temperature T (in °C) divided by the critical temperature (T-crit) at the point CP. Z-axis: element concentration C_A divided by the concentration (C_A-crit) at the critical point CP.

There is a systematic relationship between water concentration and temperature and a relationship between the concentration of various elements with water concentration and temperature, mainly in the form of Lorentzian-type curves. The critical point of the solvus curve coincides with the maximum of the Lorentzion distributed elements. The variation of elements showing such Lorentzian-type distribution shows instructive the high dynamic behavior of the supercritical fluids trapped in different melt inclusions. At this point, it is essential to emphasize that the supercritical fluid arriving at the solvus curve (especially at and immediately near the critical point) passes over in the critical and under-critical states (e.g., Ni et al., 2017 [19], Figure 1b in it). So, a more universal relationship is standing behind.

For about 20 elements, the first author and colleagues (e.g., Thomas et al., 2019) [15,16] could demonstrate the validity of such diagrams. However, revealing such curves was the first step to solving this puzzle standing behind the solvus curves, and the combined relationship to the enrichment of elements (the relationship between solvus and Lorentzian curves) has a deeper origin. Combining the two curve sets (pseudo-binary solvus and Lorentzian curves for each component (Figure 2), we see that the statement that the density of the silicate melt decreases is not continuous with the increase in the water content. At the critical point (CP), the concentration of some anions and cations increases rapidly; therefore, the density of the melt also shows a discontinuous, mostly sharp decrease. It is also noteworthy that the surface tension of a liquid decreases with increasing temperature and vanishes at the critical point (Guggenheim, 1945) [18]. If we look at the substantial increase of some elements at or near the critical point, the temperature increases to very high values of about 1000°C or more. From this, the primary temperatures and pressures represent supercritical conditions with typical properties: the viscosity of such fluid goes into the direction of zero, and the diffusivity increases in direction to infinity (as crucial properties). As already shown, the surface tension also vanishes at the critical point. An explanation for this behavior is only possible if we assume a supercritical fluid is coming directly from deep Earth’s mantle. We have found many proofs in crustal rocks, such as diamonds, moissanite, stishovite, coesite, etc. [20]. Traditionally, the miarolitic pegmatites in granites are directly related to their host granite [7,21-24]. Careful study of melt inclusions in many granites demonstrates that besides the typical melt inclusions, representing the granite crystallization (water concentrations between 1 and 12 % – see Johannes and Holtz, 1996) [25], there are also melt inclusions present, which depict a solvus curve with water concentrations between about 5 to more than 50% [26] and a critical point (CP) at the maximum of the solvus curve. A derivation of water directly from the crystallizing granite is complicated because pegmatite veins sometimes have root zones outside the granites.

Key Observations

An essential observation is the striking relationship between the solvus curves and the related Lorentzian element distribution around the solvus crest (Figure 2). However, these combinations are imperative but insufficient to state that the supercritical fluids come directly from deep in the mantle. The first proof came from an untypical pegmatite case: the prismatine rock from Waldheim/Saxony. This characteristic rock, with its prismatine crystals, has been discussed till now as a classic metamorphic rock [27]. The second author has intensively studied this rock from an independent standpoint. The prismatine contains high-temperature melt inclusions, which are very water-rich (see Thomas et al., 2022; Figures 9a and 9b in there) and have nothing to do with a poor metamorphic origin – they are atypical for granulite-facial minerals. Besides such H₂O-rich melt inclusions, the prismatine crystals also have trapped minerals, like coesite, reidite, and others from mantle deeps. These crystal remnants are mostly spheric with a very smooth (like polished) surface, demonstrating a very fast crystallization of the prismatine. The crystallization of the prismatine host is so fast that the natural crystal habitus of the included spherical crystals is wholly suppressed. The first author has a different view of prismatine crystallization. It is a local pegmatite crystallization initiated by rising supercritical fluids from the mantle. This mantle mineral-bearing supercritical fluid brings the necessary boron from the deep. The fast-crystallizing prismatine includes the mantle minerals (smooth spheres of diamond, coesite) unchanged in the crystal matrix. Of course, the surrounding rock is of metamorphic origin (Figure 3).

Figure 3: Pegmatitic prismatine zone in the granulite from Waldheim/Saxony

Columnar prismatine crystals (up to 3 cm long) are preferentially concentrated in zones that are more than 1 cm thick. In some rock parts, the concentration of prismatine crystals is very high [28], their Figures 1a to 1c).

In the meantime, we have found many examples with diamond, graphite, moissanite, and others in more crustal rocks (granites and pegmatite), for which we have often also seen the combination of solvus and Lorentzian curves [29-32]. For the Variscan Königshain granite near Görlitz/Lusatia, we [32] found magmatic epidote, which indicates a speedy ascent of the granite magma (700 to 1000 m/year). With the proof of diamond in zircon of the same rock samples, Thomas (2023a) [31] could show that the flow of a supercritical fluid initiates that fast ascent. Also, many quartz veins in this region can explained by fast-rising supercritical fluids. The solvus curves, the Lorentzian element distribution of some compounds (such as sulfate), and the sporadic appearance of diamonds and graphite in the high-temperature quartz and zircon support this preceding statement. It is not simple to find the high-temperature melt inclusion in the quartz by the more or less strong overprint due to the low-temperature fluid inclusions representing a multi-stage hydrothermal activity. An example is in Thomas et al. (2019) [15,16]. Another example is the steep dipping beryl-quartz veins related to the Sauberg tin deposit near Ehrenfriedersdorf. Figure 4 shows details of such a beryl-quartz vein from the Sauberg mine, and Figure 5 depicts one beryl crystal with a moissanite whisker in the centrum of that crystal.

Figure 4: Detail of the studied quartz-beryl sample from the Sauberg mine near Ehrenfriedersdorf: Brl – green beryl, Mlb – molybdenite, Qtz – quartz [33].

Figure 5: An about 600 μm long moissanite (SiC) whisker in the center of a beryl (Brl) crystal (crossed Nicols). Qtz – quartz [33].

This sample’s beryl and, more sporadically, quartz also contains many nanodiamonds and moissanite crystals, including and partly grown at a crustal level untypical for those crystals. This example clearly shows that unpredictable processes can work at changing the supercritical fluid into a critical/undercritical one. Nanocrystals of diamond and moissanite are germs of larger crystals. Recently, Thomas (2024a) [34] showed that in small (~2 cm) steeply inclined pegmatite veins in gneiss from drill cores near Annaberg/Erzgebirge, many nanodiamonds and graphite are present, which demonstrates that supercritical fluids rising from mantle regions are also present far away from the classic tin deposits in the Ehrenfriedersdorf region. Notably, there is a high concentration of distributed submicroscopic diamond and graphite crystallites (Thomas, 2024) in a small vertical pegmatite vein. From all observations, it follows that the composition of the supercritical fluids is highly variable from place to place. Sometimes, the typic relicts are a combination of diamond and graphite only, and sometimes, we have observed very complex parageneses (diamond, graphite, moissanite, coesite, cristobalite-X-I (high-pressure phase of cristobalite; see Černok et al., 2017) [35], and others. Careful observations of very different pegmatites and other mineralizations (Habachtal emerald deposit) have shown that nanodiamonds, graphite, and other high-pressure minerals are testimony of the supercritical fluids. Regarding the Habachtal emerald, its melt inclusions contradict the regional geology. The thesis of the involvement of supercritical fluid solves this dilemma elegantly (see Thomas et al., 2020) [17]. Also, the first author found nanodiamonds, besides graphite, in eudialyte-bearing [Na₄(Ca, Ce)₂(Fe²⁺, Mn²⁺)ZrSi₈O₂₂(OH, Cl)₂] pegmatites in the Lovozero Massif [4] (Figure 6).

Figure 6: Raman spectrum of graphite-diamond aggregate in nepheline in the eudialyte rock. The diamond line at 1324.8 cm^-1 has a FWHM of 45.7 cm^-1. The graphite main band at 1606.2 cm^-1 is remarkably high.

As a rule, the diamonds are tiny, about 4 µm in diameter. Larger spherical graphite crystals are widespread (Figure 7 and Table 1).

Figure 7: Small diamond (D) crystal with graphite (Gr) in nepheline (Ne). The crystal is 30 µm under the sample surface.

Table 1: Raman data of spherical diamond and graphite inclusions in pegmatite nepheline in eudialyte-dominant rock from Lovozero Massif (15 spherical crystals).

Phase	Position (cm^-1)	FWHM (cm^-1)	Position (cm^-1)	FWHM (cm^-1)
Diamond	1333.0 ± 8.6	81.0 ± 16.2
Graphite G			1578.6 ± 9.5	80.9 ± 10.8
Graphite D2			1604.3 ± 3.0	42.5 ± 8.0

FWHM – full width at half maximum

Interpretation

Based on innumerable results from natural samples, we could demonstrate that supercritical fluids significantly affect many mineralizations in water and energy delivery. Niggli [36] already emphasized the outstanding significance of H₂O for forming pegmatites. He wrote (page 423): “Melt solutions in which water is plentiful as an essential volatile component besides large amounts of non-volatile components are called pegmatitic solutions.” However, over the origin, the reader remains in the obscure. Ni [37] gives in his introduction to advances in the study of supercritical fluids essential hints to the effects of supercritical fluids, which covers a broad spectrum between hydrous melts and aqueous solutions with an emphasis on the enrichment of large ion lithophile elements (LILE), U-Th-Sr and REE. Sun et al. [38] show that supercritical fluids’ speciation and transport properties are essential for understanding their behavior in the Earth’s interior. According to our studies, supercritical fluids connect the deep mantle and the crust by delivery of energy, water, and various elements, e.g., coming from subducted zones. Our studies answer those open questions regarding the origin of water. There is already a lot to solve all problems related to the origin of water and the processes connected with the transition from the supercritical to the under-critical stage of water. The enrichment processes in this state, indicated by the Lorentzian type of element distribution, are a further open problem. Element enrichment in a relatively small room by the factors of 10⁴ to 10⁵ is not seldom. Is there communication between the particles, like the quantum entanglement in the supercritical state?. We have seen that mantle minerals like nanodiamonds are already present in the old Volyn pegmatites (1760 Ma) [39], the pegmatites from the Lovozero Massif of Devonian age and the younger ones like the Variscan Erzgebirge and the Königshain Massif [20], Furthermore, it follows the question of the time and intensity of the ascending supercritical fluid: sporadic or continuous?

Acknowledgment

We thank many coworkers and other scientists in the past who have contributed to the supercritical fluids and their proof in natural samples. A special thanks go to Huaiwei Ni (Hefei, China), Yicheng Sun (Nanjing), and Jim Webster (New York).

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Mind Genomics and Today’s Realpolitik: Considering the ‘Invasion’ of Single Young Men at the US Southern Border from the Point of View of What Mind-sets Might Exist and What to Consider

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DOI: 10.31038/JIAD.2024114

Abstract

A combination of Mind Genomics to understand motivation coupled with Idea Coach (artificial intelligence module within Mind Genomics) was used to create synthetic mind-sets which might describe young males illegally crossing the US southern border. The paper shows how AI can provide information to spur critical thinking when provided with a description of the situation and the motivation for the illegal crossing. The authors suggest that the world of law enforcement might benefit by using these procedures to facilitate critical thinking.

Introduction

As of this writing, February 2024, the United States is experiencing a never-before-situation at its borders. The southern borders of the United States, especially those in Texas, are being inundated by migrants, many of whom are young, unaccompanied men, who slip into the United and end up disappearing inside the United States. Many of these people disappear entirely. Others go to court and are allowed to say pending their case.

Up to now, the material just presented recognizes a problem emerging, namely the escape of many unregistered aliens through what has turned out to be an exceptionally porous border, manned by seriously understaffed border patrols and immigration officers. The consequence is not unexpected. Many people believe that through such unmonitored immigration, there is a good chance an at alien army my be coming in, this army not necessary friends o the United States, the company into which they are disappearing.

The objective of this paper is to demonstrate how AI can be used to formulate hypotheses about the nature of what might be happening at the southern border, and then to demonstrate the change in AI-based ‘conclusions’ when the motive for the invasion includes intending to harm the United States.

History of the Approach

The tool used here is Idea Coach, an AI-empowered program embedded in the Mind Genomics platform [1]. In turn, Mind Genomics is a platform which specializes in the analysis of human judgments, doing so by presenting the respondent (survey taker) with a variety of vignettes, and for each vignette, obtaining a rating on defined scale. The vignettes themselves are combinations of simple, easy-to-read statements, called elements. The vignettes are constructed according to a plan known as an experimental design. Each respondent evaluates a unique set of 24 vignettes [2]. The ratings are then transformed to a simple Yes/No scale. The final steps are to use simple OLS (ordinary least-squares) regression at the level of each respondent to relate the presence/absence of the elements to the transformed ratings. The coefficients of the equations become the tool to understand the mind of the respondent. This simple analysis shows immediately what elements ‘drive’ the ratings, and by so doing reveal the underlying mind of the respondent with regard to the specific topic. The final analysis clusters the respondents into different groups based upon similar patterns of coefficients.

The foregoing approach has been embedded into the aforementioned Mind Genomics platform (www.bimileap.com). The original format of Mind Genomics required that the user provide a set of four questions ‘which tell a story’, and then ‘four answers to each question.’ It was the answers which the respondent evaluated, after these answers (now called elements) were mixed and matched to create the vignette.

AI was necessary to help the user think of questions and answers. Over the period of several years, it became increasingly obvious that users of Mind Genomics both liked the approach but were terrified of the requirement to come up with questions and answers. Figure 1 shows the request to provide four questions. This request was a wall to many prospective users because, quite simply, it was daunting. People were often good at answering questions but not at formulating questions to tell a story.

Figure 1: The set-up screen for Mind Genomics studies. Panel A requires the user to provide four questions which tell a story. Panel B shows the Idea Coach, giving the user the opportunity to describe the topic, and turn receive 15 questions generated by AI.

The incorporation of AI into the process of questions and answers increased the acceptance of the Mind Genomics platform, for at least four reasons:

The process no longer stymied the user. A simple ‘squib’ in Figure 1, Panel B, sufficed to generate 15 questions.
The same process occurred in the generation of elements. Once the user selected four questions and put those questions into the template (Figure 1, Panel A), the Idea Coach was able to return 15 answers to each question selected.
The process was rapid, with the suggested sets of 15 questions or 15 answers to a question returning in about 20 seconds.
The user could edit the squib to change the nature of the questions, or edit the selected questions to change the nature of the answers

The incorporation of AI as Idea Coach ended up producing Idea Books, compilations of questions produced in response to the squib (Figure 1, Panel B), as well as compilations of answers produced in response to each question. Each page in the Idea Book corresponded to one iteration, whether the 15 suggested questions resulting from a request written out in the squib, or 15 answers resulting from the selection of a question. It was not unusual to generate Idea Books of 10+ tabs.

In addition to the sets of questions or answers on each page, the AI was given the task of summarizing the material on each page, viz., the questions or answers. The result was other insights, such as key ideas, themes, perspective, what is missing, interested audiences, opposing audiences, and innovations. Each of the foregoing was given its own section on the page in bold type, and then the relevant AI summarization provided.

The foregoing process required about 30 minutes in total from beginning the set up of the Mind Genomics experiment to the creation of a book with say 20 pages. The process itself was quick, the results were easy to obtain, and the iterations themselves became a source of learning, the Idea Book turning into a resource book for further work.

Over time, and as the process became easier, the process first became rigid as practitioners using Mind Genomics followed the path laid out, with simple questions posed to the AI embedded in Idea Coach. The only modifications during the early days of Idea Coach, the year 2022-2023, was the expansions of the question, so that the questions would have a certain number of word (~ 10-15), that the questions would be interesting, and that the questions could be understood by a young person. The same ‘editing’ of requests was done for the questions themselves in order to generate answers which ‘were not lists, but rather statements which could lead to a discussion’. All of these were style questions, rather than substance questions. The happy consequence was that the Idea Book was richer in content, the questions and answers more instructive, and the process enjoyable to the user, who could practice writing different requests about the style and structure of the output to be generated by Idea Coach

Advancing Insights through Deeper Interactions with AI through the Idea Coach

During the course of working with Idea Coach, author Mulvey expanded the nature of the squib, and generated unexpected and deeper results. Rather than simply specifying the nature of the question or answer in terms of style (viz., number of words, age of reader, style to engage the reader rather than list options), Mulvey added a request into the squib. That request was to provide some additional answers to the question. That is, the squib or input to the AI embedded in Idea Coach contained a request for additional structure in the question, rather than just a question alone. The approach is conceptually similar to the creation of synthetic data, in this case synthetic mind-sets [3].

Idea Coach returned with unexpectedly deeper questions. The output to this ‘expanded request’ was more like a summary of a situation than simply suggested questions. Exploration of alternative ways to expanded the input to Idea Coach quickly revealed that the AI could be requested to a far deeper analysis. And so, the approach was born which lies at the foundation of this paper.

The paper itself explores how the Idea Coach provides more information when given a detailed instruction. Table 1 shows the text as it appears in the Idea Book returned to the respondent. The top section in bold shows what the user types into the squib. The rest of the table shows what it returned to the user by Idea Coach when the Idea Book is completed.

Table 1: Output from Idea Coach for the first request, where the hostile goals of the border crossing men are not revealed.

The first part of the input to Idea Coach is simple and direct, describing what is happening., and a simple request for suggestions: The topic is: Invasion of the United States at the southern border cross by unmarried young men of military age. How can we prevent these people from starting massacres at unarmed gatherings throughout the United States if they are truly ‘invading us’.

The second part of the input to Idea Coach is the set-up hypothesis that there exist three mind-sets. These mind-sets are not named, and indeed no information is given about any conjectures regarding the mind-sets. It will be the job of the AI in Idea Coach to suggest the mind-sets. We will see that the suggested mind-sets returned by AI ends up concurring with additional information provided by the user. The actual text is straightforward: We believe that there are three different mind-sets of these young men. The user can change the number of mind-sets and add more information about the mind-sets. Each change generates a new set of responses, further serving as an educational and preparedness tool for the user. and can be modified by the user to see what happens when the number of mind-sets is changed.

The third part of the input to Idea Coach is the instruction to answer a set of eight specific questions for each to-be-named mind-set:

For each mind-set in turn, answer these specific questions:

What is the name of the mind-set
What is the goal of the mind-set specifically with regard to being in the United States
What are they likely to say to an immigration official when they are caught
What will convince them to go through legal channels to become regular citizens
How can we recognize them… give four indications to help recognize the mind set to which they belong
If unrecognized, what will they likely do in three months after they have entered illegally into the United States
If unrecognized, what will they likely do in six months after they have entered illegally into the United States
What will make them want to identify themselves to officials in the United States

Do the above answers for each mind-set separately, answering all questions 1-8 for each mind-set in turn

The fourth and final instruction to Idea Coach focuses on the style of the suggestion to be given by Idea Coach:

Make the answers interesting to read, and easy to talk about to other people

Make the answers as realistic as possible

Recognize that the answers will be shared with officials in the United States

Section B in Table 1 shows the immediate work-product of the AI embedded in Idea Coach. Section B returns with 30 seconds. Generally, but not always, Idea Coach provides precise sets of answers, following the instructions written in the squib. The user is free to re-run the Idea Coach many times, changing the squib desired. (That change will be shown in Table 2, where the input to Idea Coach will add information about the migrants ‘wanting to harm America’.) Each iteration of Idea Coach generates a mix of new ideas and old ideas

Section C in Table 1 shows a set of summaries created by AI, with these summaries taking into account all of the information in Sections A and B, respectively. The summarizing queries in Idea Coach are fixed, but the information in each iteration tends to be partially unique, so that the summarizations will differ from iteration to iteration.

When looking at different sections of Table 1 the reader should keep in mind that within less than a minute the user has gone from a set of questions to a set of answers, and in a few more minutes from a set of answers to a set of summarized generalities. It is also important to keep in mind that the typical Idea Book does not stop with one or two iterations, but may go on for dozens of iterations, each done without much effort, each done to satisfy one’s curiosity about a particular issue, not only about the general topic.

Iterating and Adding Information about Motives for a More Targeted Analysis

As stated above, a key benefit of the Mind Genomics approach as empowered by the Idea Coach emerges from the ability to modify the squib or request given to the underlying AI. Table 1 showed the three mind-sets without any specification of who the mind-sets are, other than the general concern about a possible massacre in a public place.

Tables 2-5 show the descriptions of the mind-sets developed by AI when the invading males are further specified as to their assumed motives. The issue now is to see how these three mind-sets are described. For the purposes of this paper, the focus is simply on the types of descriptions which emerge when additional information is provided to the AI embedded in Idea Coach. In a sense the descriptions of the mind-sets generated in Tables 1-5 can be looked at an exploration of how AI can put features onto basic descriptions.

Table 2: Three mind-sets emerging from AI when the motivation is to start a family in the United States

Table 3: Three mind-sets emerging from AI when the motivation is to start working and then become citizens

Table 4: Two mind-sets emerging from AI when the motivation is to get jobs and bring in their family living in poverty

Table 5: Three mind-sets emerging from AI when the motivation is to harm the United States with gang warfare

Discussion and Conclusions

The ingoing rationale for this study was to demonstrate that a new opportunity to understand behavior has evolved from the incorporation of AI through Idea Coach into the basic thrust of Mind Genomics. The original objectives of Mind Genomics focused on understanding motivations and decision ‘rules’ for different types of people, with these rules emerging from material taken from the granular, everyday world. The development of AI made it possible to accelerate the process by producing a way to help people ask questions and create answers for that question.

The introduction of AI also made it possible to go into directions not thought of before, or if thought of, then consigned to the world of the theoretical. We are talking here about ‘what if’ questions. What if we could ascribe basic motivations to people, almost making the task which involves synthetic people, rather than real people. This paper shows what can be done by creating synthetic people, simply by telling AI that there are three mind-sets, giving some background, and then systematically varying some aspect of that background. Tables 1-5 show what happens when the user moves from no motivation stated to a variety of different motivations.

Up to now a major focus of AI in law enforcement has been to detect patterns in the transfer of money and other objects, almost the combination of Big Data and the Internet of Things [4-7]. The use of AI to construct synthetic mind-sets for law may be in progress but is not yet mainstream. On the other hand, the use of AI to construct synthetic people for surveys is beginning to become mainstream, at least in the market research community. There is every reason to assume that the use of AI to construct scenarios and synthetic people, as well as synthetic mind-sets, will become mainstream, and perhaps even significant. To the degree that the approach presented here becomes a tool for critical thinking in law and public policy, we may expect to see the approach presented here proliferate and improve thinking as well as public policy.

References

Moskowitz H, Todri A, Papajorgji P, et al. (2023) Sourcing and vetting ideas for sustainability in the retail supply chain: The Contribution of artificial intelligence coupled with Mind Genomics. International Journal of Food System Dynamics 14: 367-380.
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Yavorsky MA and Mikheeva SV (2022) The Use of Artificial Intelligence Technologies and Big Data in Law Enforcement. In Proceedings of the International Conference Engineering Innovations and Sustainable Development 669-675. Springer International Publishing.

Gaza as a Middle East Singapore – Enhanced Visioning of Opportunities Suggested by AI (Socrates as a Service)

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DOI: 10.31038/JIAD.2024113

Abstract

The project objective was to explore the opportunity of Gaza as a new Singapore, using AI as th source of s suggestions to spark discussion and specify next steps. The results shows the ease and directness of using AI as a ‘expert’ when the materials presented here were developed through the Mind Genomics platform BimiLeap.com and SCAS (Socrates as a Service). The results show the types of ideas developed, slogans to summarize those ideas, AI-developed scales to profile the ideas on a variety of dimensions, and then five ideas expanded in depth. The paper finishes with an analysis of types of positive versus negative responses to the specific solutions recommended, allowing the user to prepare for next steps, both to secure support from interested parties, and to defend against ‘nay-sayers’.

Introduction

Europe had been devastated by Germany’s World War II invasion. The end of World War II left Germany in ruins. The Allies had deported many Germans, leveled Dresden, destroyed the German economy, and almost destroyed the nation. From such wreckage emerged a more dynamic Germany with democratic values and a well-balanced society. World War II ended 80 years ago, yet the effects are being felt today. The Israel-Hamas battle raises the issue of whether Gaza can be recreated like Germany, or perhaps more appropriately Singapore Can Gaza become Middle East Singapore? This article explores what types of suggestions might work for Gaza to become another Singapore, with Singapore used here as a metaphor. Businesses have already used AI to solve problem [1,2]. The idea of using AI as an advisor to help with the ‘social good’ is alo not new [3-5]. What is new is the wide availability of AI tools, such as Chat GPT and other larger language models [6]. ChatGPT are instructed to provide replies to issues of social importance, specifically how to re-create Gaza as another Singapore. As we shall see, the nature of a problem’s interaction with AI also indicates its solvability, the acceptance by various parties and the estimated years to completion

The Process

First, we prompt SCAS, our AI system, to answer a basic question. The prompt appears in Table 1. The SCAS program is instructed to ask a question, respond in depth, surprise the audience, and end with a slogan. After that, SCAS was instructed to evaluated the suggestion rate its response on nine dimensions, eight of which were zero to 100, and the ninth was the length of years it would take this program or idea to succeed.

Table 1: The request given to the AI (SCAS, Socrates as a Service)

tab 1

The Mind Genomics platform on which this project was run allows the user to type in the request and press the simple button and within 15 seconds the set of answers appears. Although the request is made for 10 different solutions, usually the program returns with far fewer. To get more ideas, we simply ran the program for several iterations, meaning that we just simply pressed the button and ran the study once again. Each time the button was pressed by the user, the program returned anew with ideas, with slogans and with evaluations. Table 2 shows 37 suggestions. emerging from nine iterations, the total time taking about 10 minutes. The importance of many iterations is the AI program does all the ‘thinking,’ all the ‘heavy lifting’. Table 2 is sorted by the nature of the solution, the categories for the sorting provided by the user. Each iteration ends up generating a variety of different types of suggestions, viz., some appropriate for ‘ecology’, others for ‘energy’, other for ‘governance’ and so forth. Each iteration came up with an assortment of different suggestions. Furthermore across the many iterations, even the ‘look’ of the output change. Some outputs comprised detailed paragraphs, other outputs comprised short paragraphs. Looking at the physical format gave a sense that the machine seemed to be operated by a person whose attention to details was oscillating from iteration to iteration. Nonetheless, the AI did generate meaningful questions, seemingly meaningful answers to those questions, and assigned the ratings in a way that a person might. The table is sorted by the types of suggestions. Thus the same topic (e.g., ecology) may come up in different iterations, and with different content. The AI program does not make any differentiation, but rather seems to behave in a way that we would call ‘does whatever comes into its mind.. It can be seen that by doing this 10, 20, 30 times and having two or three or four suggestions for each, the user can create a large number of alternative solutions for consideration. Some of these will be, of course, duplicate. Many will not. A number will be different responses, different points of view, different things to do about the same problem, such as economy.

Table 2: The suggestions emerging from eight iterations of SCAS, put into a format which allows these suggestions to be compared to each other.

tab 2(1)

tab 2(2)

tab 2(3)

tab 2(4)

tab 2(5)

Expanding the Ideas

The next was to selected five of the 37 ideas, and for each of these five ideas instructed AI (SCAS) to ‘flesh out’ the idea with exactly eight steps. Table 3 shows the instructions. Tables 4-7 show the four ideas, each with the eight steps (left to SCAS to determine). At the bottom of each table are ‘ideas for innovation’ provided by SCAS when it summarizes the data at the end of the study. The Mind Genomics platform ‘summarizes’ each iteration. One of the summaries is the ideas for innovation. These appear at the bottom section of each of the five sections, after the presentation of the eight steps.

Table 3: The prompts to SCAS to provide deeper information about the suggestion previously offered in an iteration.

tab 3

Table 4: The eight steps to help Gaza become a tourism hotspot like Singapore

tab 4

Table 5: The eight steps to make Gaza ensure inclusive and sustainable growth for all its residents

tab 5

Table 6: The eight steps to help Gaza improve its economy and living standards

tab 6

Table 7: The eight steps to help Gaza promote entrepreneurship and small business development

tab 7

The Nature of the Audiences

Our final analysis is done by the AI, the SCAS program in the background after the studies have been complete. The final analysis gives us a sense of who the interested audiences might be for these suggestions and where we might find opposition. Again, this material is provided by the AI itself and the human prodding. Table 8 shows these two types of audiences, those interested versus those opposed, respectively.

Table 8: Comparing interested vs opposing audiences

tab 8

Relations among the ‘Ratings’

The 37 different suggestions for many topic areas provides us with an interesting set of ratings assigned by the AI. One question which emerges is whether these ratings actually mean anything. Some ratings are high, some ratings are low. There appears to be differentiation across the different rating scales and within a rating scale across the different suggestions developed by AI. That is, across the 37 different suggestions, each rating scale shows variability. The question is whether that is random variability, which makes no sense, or whether that is meaningful variation, the meaning of which we may not necessarily know. It’s important to keep in mind that each set of ratings was generated entirely independently, so there is no way for the AI to try to be consistent. Rather, the AI is simply instructed to assign a rating. Table 8 shows the statistics for the nine scales. The top part shows the three decriptive statistics, range, arithmetic mean, and standard error of the meant. Keeping in mind that these ratings were assigned totally independently for each of the 37 proposed solutions (Table 2), it is surprising that there is such variability. The second analysis shows something even more remarkable. A principal components factor analysis [7] enables the reduction of the nine scales to a limited set of statistically independent ‘factors’. Each original scale correlates to some degree with these newly created independent factors. Two factors emerged. The loadings of the original nine scales suggest that Factor 1 are the eight scale of performance as well as novelty, whereas Factor 2 is years to complete. The clear structure generated across independent ratings by AI of 37 suggestions is simply very clear, totally unexpected, and at some level quite remarkable. At the very least, one might say that there is hard-to-explain consistency (Table 9).

Table 9: Three statistics for the nine scales across 37 suggested solutions

tab 9

Discussion and Conclusions

This project was undertaken in a period of a few days. The objective was to determine whether AI could provide meaningful suggestions for compromises and for next steps, essentially to build a Singapore from Gaza. Whether in fact these suggestions will actually find use or not is not the purpose. Rather the challenge is to see whether artificial intelligence can become a partner in solving social problems where the mind of the person is what is important. We see from the program that we used, BimiLeap.com and its embedded AI, SCAS, Socrates as a Service, that AI can easily create suggestions, and follow up these suggestions with suggested steps, as well as ideas for innovation. These suggestions might well have come from an expert with knowledge of the situation, but in what time period, at what cost, and with what flexibility? All too often we find that the ideation process is long and tortuous Our objective was not to repeat what an expert would do, but rather see whether we could frame a problem, Gaza as a new Singapore, and create a variety of suggestions to spark discussion and next steps, all in a matter of a few hour. The potential of using artificial intelligence to help spark ideas is only in its infancy. There’s a good likelihood that over the years as AI becomes quote smarter and the language models become better, suggestions provided by AI will be far more novel, far more interesting. Some of the suggestions are interesting, although many suggestions are variations on the ‘pedestrian’. That reality not discouraging but rather encouraging because we have only just begun. There’s a clear and obvious result here that with the right questioning, AI can become a colleague spurring on creative thoughts. In the vision of the late Anthony Gervin Oettinger of Harvard University, propounded 60 years ago, we have the beginnings of what he called T-A-C-T, Technical Aids to Creative Thought [8]. Oettinger was talking about the early machines like the EDSAC and programming the EDSAC to go shopping [9,10]. We can only imagine what happens when the capability shown in this paper falls into the hands of young students around the world who can then become experts in an area in a matter of days or so. Perhaps solving problems, creative thinking, and even creating a better world will become fun rather than just a tantalizing dream from which one reluctantly must awake.

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Mind Genomics and Today’s Realpolitik: Considering the Conflict between Texas and the Federal Government from the Point of View of What Mindsets Might Exist and What to Consider

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DOI: 10.31038/JIAD.2024112

Abstract

This paper deals with how AI can summarize issues emerging from the newly developing confrontation between Texas and the US Federal Government regarding unrestricted migration across the southern US border. The paper shows how AI embedded in the Mind Genomics platform can provide a mechanism whereby a person can profoundly explore subjective thinking about a topic. The paper presents the verbatim result of an easy-to-understand, easy-to-do, and very affordable investigation, one which required less than 36 to conceptualize, implement, understand, and ‘work up’ for publication. The combination of Mind Genomics and AI in the user-friendly Idea Coach promises a new capability to democratize critical thinking, world-wide.

Introduction

Since the informal opening up of the southern US Border, the news sources have been filled with innumerable accounts of migrants coming across the border and escaping into the United States. The issues involved in this wholesale unwanted, unplanned for and frankly dangerous entrance of hordes of strangers into the US has erupted into conflicts between states and now a conflict between the state of Texas and the US Federal Government. The issue is so grave that the government in Texas is announcing that it will maintain its protective system of wires, in defiance of the United States Federal Government. As one might expect, the issues are so important, so severe that within the past month alone (January 2024) dozens of treatments of the issue have appeared in Google Scholar, remarkable for a topic [1-6]. The sheer volume of published material on the topic, the continuing ‘buzz’ of the social media, the incessant reporting by today’s media giants, and the unspoken concerns of the ordinary citizen suggest the need for a better way to understand the topic. It is to address that need that this paper was written, more from the desire to see how far AI could go to help the citizen ‘understand’, as well as how far AI could go to make a scholar contribution to civil society. This ‘experiment in AI’ looks at the situation from the point of view of what might be called the ‘Mind of Texas’. The underlying structure of the AI study is to present the situation, then suggest that there are different mind-sets about this topic, mind-sets from the point of Texas, but not to name these mind-sets. Rather, the AI is instructed to name the mind-set, state what the mind-set believes to be the proper action, how does the mind-set support that belief, and then assign a rating to the strength of the case that Texas is making with that mind-set.The important thing to keep in mind is that the effort represents a new way of thinking about AI and political issues. Rather than summarizing information to answer a specific question, AI is tasked with identifying new to the world groups, not given anything. Furthermore, for each group, the AI is tasked with finding out how they think, and finally and most uniquely, estimating the strength of the argument.

The Contribution of Mind Genomics and the Impetus of AI to Solve the Problem of Critical Thinking

The origin of the material reported here can be traced to the emerging science of Mind Genomics, colloquially the ‘science of the everyday’. The inspiration for Mind Genomics was the desire to understand how ordinary people thought about the ordinary events of their day, the granular aspects of their quotidian existence to put in a way which may have more gravitas. Cognitive psychology recognizes that people make decisions in different ways but focuses on the person making the decisions rather than on the specific topics about which the decisions are made. Mind Genomics works with combinations of elements, these elements being simple descriptions of aspects of a thing or a situation. The combines these elements into small vignettes, combinations of elements, doing so according to a scheme which prescribes the precise combinations. Respondents, or in other disciplines so-called ‘survey-takers’ then read each vignette and rate the vignette, that combination of messages. The respondent may read several dozen vignettes, one vignette at a time, and then rate the vignette on a scale. Once the data are collected, the researcher deconstructs the rating assigned to the vignette into the part-worth contribution of each of the elements. To the respondent the task is simple, although often boring. The elements are combined in different ways for the respondents, so that each respondent evaluates a different set of vignettes. The mathematical structure of the vignettes is the same form one respondent to the other. The only thing which changes is the particular set of combinations. This ability to give people different combinations of the same elements, but in reality the underlying structure is the same, is a feature of Mind Genomics. When doing the task the respondent, having been presented with these seemingly random combinations, stops trying to figure out the ‘underlying game’, stops trying to outguess the researcher, and ends up simply guessing, which turns out to be what was desired in the first place. The happy result is that the way the respondent thinks about the topic ends up being revealed by statistics (OLS or ordinary least-squares regression). The system works because it mimics nature. Nature continues to present to us vignettes, combinations of simple stimuli. In order to survive we have to pick out through this blooming, buzzing confusion in the words of the eminent 19^th century Harvard psychologist, William James. The desire to understand how different elements drive the response is attractive to researchers because these elements possess ‘meaning.’ When the researcher presents combinations of phrases to the respondent, the combination does signify something, and elicits a responses, even though the combinations may be put together in what seems to be a random way. When the researcher combines the phrases according to an underlying ‘experimental design’ which specifies the particular combinations, there is nothing in the design which forces a coherent meaning. Yet, for the most part, respondents have no problem finding meaning in the combination. When asked to rate the combination, the vignette, most respondents may be at first perplexed about what to do, but within one or two exposures the respondents cease being perplexed and go about the task of reading and rating the vignettes. The behavior is almost like ‘grazing’ for meaning. That is, the respondent looks at the vignette, extracts the information needed to decide, assigns a rating and moves on. From literally thousands of studies following the use of prescribed experimental designs and inexperienced panelists who don’t know what to do, the data which emerge makes sense. What appeared to be a random set of combinations, perhaps even a different set for each respondent, all combinations, all vignettes constructed by design quickly gives way by regression analysis to a clear picture of what specific messages ‘drive’ the response. The data are often clear even with the whole panel whose individuals may possess different points of view. The pattern emerges. When the data from the panelists is analyzed by clustering, the different points of view end up being separated into mind-sets, clusters of people showing different patterns of elements which drive the responses. The foregoing has presented a short history of a growing science, beginning in the world of food [7], beginning with commercial issues, soon morphing inexorably to issues of social importance, whether that be society, ethics and morals, just to name a few [8]. Over time Mind Genomics thinking further expanded its reach into the law [9], education [10] and medicine [11] continuing to do so today, as will be shown by the study discussed here.

The Introduction of AI to the World of Systematic Experimental Design of Ideas

The migration of research away from simple surveys to the evaluation of mixtures of messages was introduced to th research world in 1993, at the annual ESOMAR research congress, held in Copenhagen, Denmark [12]. The interest by market research companies was awakened, and a number of companies licensed the technology, which was run on a PC, and required local set up, but had automatic analysis. The one continuing issue in the licensing business was the need develop the messages. Although the researchers were familiar with qualitative research (REF), the task of developing meaningful messages ‘on demand’ and for different topics began to stress the researchers, and in turn their clients. The requirement to develop a specific set of messages for a topic, messages with substance, proved harder to fulfill than simply creating a questionnaire with general ideas (e.g. how important is XYZ?) The problem of developing elements to put into the system continued to plague the growth of Mind Genomics, even though with experience most practitioners felt comfortable developing the requisite set of messaging. Over time the Mind Genomics system developed into a templated process. The user was instructed to assign the study a name, to think of four questions which tell a story, and then for each question to think of four answers. Figure 1, Panel A shows the request to provide four questions which tell a story. Creating a template helped the process of coming up with messages, although users of the program, www.BimiLeap.com, often reported that being confronted with the empty template and a request to provide questions was unnerving. Once the user developed the questions, however, providing answers became far less stressful. It became increasingly clear that the ability to think in a creative fashion seemed to have diminished, at least for the development of questions which ‘told a story.’ At the same time, it became obvious that people with little experience rarely complained about providing answers to questions that they had developed or that were posed to them by someone else. The introduction of artificial intelligence in 2022 through Chat GPT provided a breakthrough. The use of AI had been already demonstrated several years before [13]. The task was detailed, and not sufficiently quick, certainly not the ‘turnkey’ process, although for its time the process proved excellent. What was need was a turnkey system. Chat GPT by Open AI [14] provided that system, one that was incorporated into the BimiLeap platform, which supported the now more mature science of Mind Genomics. AI was introduced in the form of a tool called Idea Coach, which required the user to write out the problem (Figure 1, Panel B), which would then result in the return of 15 questions. The process could be repeated, the expression of the problem could be edited, and the process could be repeated ad infinitum. Idea Coach allowed the user to select questions, populate the template, and then do the same thing to create each of the four answers, to each o the four questions. In other words, the Idea Coach embedding AI made the process faster.

Figure 1: The templated request for four questions (Panel A), and the box where the user can communicate the request to AI embedded in the Idea Coach (Panel B).

Moving Beyond Questions and Answers to AI as a Deeper Guide to Critical Thinking

The initial use of Idea Coach focused on creating questions, then answers to the questions, and finally doing the ‘experiment’ with real people after having selected the four questions, and the 16 answers (elements). Over time, however, the appeal of AI grew, along with the unexpected discovery that AI would generate more when the squib (Figure 1, Panel B) was given more detail instructions, and asked to play a greater role. Table 1 shows what happens when the request for questions is replaced by a composition about a topic, specifically the issue of the Texas border. The input to the AI is far deeper, far more extensive. Table 1 shows a virtual briefing, requiring AI to carry out specific actions and exhibit critical thinking. The initial experience with this approach simply requested AI to identify mind-sets, rather than simply to prepare a set of questions. Some months later the request for AI was far deeper, with many more levels. The ability to request answers with ‘critical thinking’ became a newly discovered benefit in the Mind Genomics platform. It was easy to put together these ‘squibs’ to put into the Idea Coach. Once the squib was created the user needed only to request one iteration after another, with the AI in Idea Coach returning with either new results, or occasionally excuses why the request could not be fulfilled anew, even though the request had just been fulfilled three times, each time with different types of answers .As a second benefit, it was quite easy to switch to an edit mode, revise the squib and try again, a capability which led to a half a dozen to two dozen iterations and edits in a matter of 30 minutes.

Table 1: The request to AI to provide ‘critical thinking’ about the border issue between Texas the US Federal Government.

Table 2 shows the unexpected outcome. The request to the AI was to identify 18 different mind-sets, and to answer four questions (name of mind-set; believed ‘right action’, support for belief; numerical estimate of the strength of case). No other information was provided to the AI in Idea Coach. Yet, with 20 seconds of the request, the following set of 16 mind-sets (not the requested 18) was provided. The mind-sets may or may not be accurate. What is impressive, however, is both the speed at which this request was fulfilled, and the quite deep nature of the request, including the totally unexpected assignment of scores for the strength of the, or here really the ‘strength of the opinion in a court of law.’ Whether or not these conjectured mind-sets are correct is not relevant for right now. The important thing to keep in mind is that the AI allows the user to begin to think in a new manner. Rather than stranding the user with questions which require the user to enter into a potentially stressful situations, viz., required to produce ideas, the Idea Coach produces material which engages the user in a dialog. The user can discuss the conjectures produced by the AI. The AI is becoming a tutor. What is even more interesting is the ‘seeming’ ability of AI to produce estimates of the strength of the different arguments held by the mind-sets, as shown by the rating of the ‘Strength of Texas’ case’, ranging from a high of 90% (Mind-Set #6; The State’s Rights Advocate) to a low of 15% (Mind-Set #16; The Free Movement Advocate). The reasons for the ratings of strength of argument are not know, but the numbers themselves can serve as springboards for discussion.

Table 2: The 16 returned mid-sets, showing the name of the mind-set, information about the attitudes of the mind-set, and the hypothesized strength of the Texas argument, as estimated by the AI tool.

The Idea Coach has been programmed to provide a detailed summarization of the information at the end of the iteration. Once the steps are completed which create the ‘questions’ and ‘answer’ (viz., silos or categories and their elements) the Mind Genomics platform assembles all the iterations, and for each iteration summarizes that iteration using a series of questions. The summarized material, including that shown in Tables 1 and 2, is returned by email in an Excel file. Each tab of the Excel file corresponds to a different iteration.

First Set of Summarizations – Viewpoints and Audiences

Summarization 1 is alterative viewpoints. For this effort, the Idea Coach returned with five general viewpoints, looking at the problem of migration from a variety of points of view. The Alternative viewpoints are general in nature. Once again the alternative viewpoints are presented in descending order of strength for Texas’ case. Summarization 2 describes ten different groups who would be interested in the topic, and why specifically they would be interested in the topic. Summarization 3 describes six different groups who would be opposed to dealing with the topic for their own specific reasons. Once again it is obvious that the AI produces seemingly ‘reasonable’, ‘thought-out’ analyses. Even if the analyses are incorrect, they serve as a springboard for discussion. Once again it is important to keep in min that these summarizations are done quickly, inexpensively, and can be done at an ‘industrial-scale’ to produces specific ‘knowledge’ on virtually any topic (Table 3).

Table 3: Three different ways of approaching the issues, by considering the topic from the mind of the person. These three ways are alternative viewpoints (general viewpoints), interested audiences (the topic is interesting to discuss), and opposing audiences (the topic is not of interest to them, and they would rather see the topic ‘shelved’ or otherwise eliminated.)

Second Set of Summarizations – Ideas, Themes, Perspectives

This second set of summarizations focuses on the different ideas which emerge from the AI deep thinking, or deep simulation. The first summarization deals with the key ideas emerging from the exercise. The AI embedded in the Idea Coach takes the set of suggestions (questions, proposal of mind-sets), analyzes the text, and returns with the different basic ideas. In this way the summarization pulls together the different points into what could be a smaller set of more general ideas. The second summarization, themes, further reduces the information almost to a summary of the summary. The third summarization, perspectives, moves from reducing the ideas to expanding the ideas into points that can be discussed. At this point the AI has taken the user through ideas, through reducing the ideas to a limited set, and then having digested the information returns with points for further discussion (Table 4).

Table 4: Three different ways of sharpening the ideas. Key Ideas show the topics extracted from the different mindsets. Themes further reduce the information to a small set of general ideas. Perspectives challenge the reader to rethink the topic with greater insight, presenting arguments, both the pros and the cons.

Third Set of Summarizations – What is Missing, and Suggestion for Innovation

The objective of this third set of summarizations revolves around using Mind Genomics and Idea Coach to drive innovation. The reality of having AI suggest both what is missing, and design innovations becomes the capstone of the effort. The sheer richness of information in Table 5 is remarkable, especially recognizing that the AI embedded in Idea Coach was given no starting information. Rather, it is simple the repeated summarizations emerging from one ‘briefing’ to the AI (Table 1) which generated this set of suggestions for innovation. Of course, the suggestions must be vetted for feasibility, and tested for acceptance by people.

Table 5: The search for ‘what is next’. The top of Table 5 shows what’s missing. The bottom shows the AI suggestion for innovative ideas.

Discussion and Conclusions

The project reported here represents what one can accomplish with today’s AI, specifically the AI embedded within the Mind Genomics platform, www.bimileap.com. The simple approach of presenting a current issue and the plethora of information, suggestions, directions, and so forth becomes ever more remarkable when one realizes the simplicity of execution. The issue no longer becomes the ability to sit in a room of experts who have developed an understanding of the situation, that understanding coming after years of involvement, that understanding being the possession of the few, and of course in the world of homo economicus, that understanding coming at a high price tag. Rather, knowledge, direction, opportunity now lie at the hands of amateurs, those who are to inherit the future, rather than to profit from their experience. To end this paper, it is important to recognize that one can do the type of work here for virtually any topic where the mind of people is the driving force. The opportunity is to create a massive database of the mind, dealing with hundreds or even thousands of topics, these topics being general issues facing people, or being specific issues facing just a few people. One can only imagine what will happen to a world where anyone, anywhere can avail themselves of this technology as part of their education, even at the level of an elementary school student (see Mendoza et. al., 2023). The future in that world remains to unfold, but the tools are already here.

References

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Mind Genomics, Artificial Intelligence and Today’s Realpolitik: The Disparity between the Rich and the Poor as Manifested by Mind-sets and by Slogans for the Upcoming 2024 Presidential Election

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DOI: 10.31038/JIAD.2024111

Abstract

A description of today’s financial disparities between the rich and the poor was fed to Idea Coach (AI) in the Mind Genomics platform (www.bimileap). Idea Coach was instructed to generate eight different ‘mind-sets’ of people based upon the description of the disparities, and to answer a variety of questions about these mind-sets. The eight mind-sets and their AI-generated features were fed to a second run of Idea Coach to generate slogans for a presidential campaign, assuming different factors, such as the year of the campaign, the nature of the person who would be the recipient of the slogan, etc. The exercise revealed the potential for deeper understanding of social issues, as well as the prospect of asking what-if questions in real time, low cost, for any topic that can be explicated simply and directly. The exercise further revealed the ability of AI to synthesize additional insights, returning all of the information in an Excel-formatted, easy-to-read ‘Idea Book.’ The process takes minutes for each iteration with the Idea Book combining all of the iterations from one run and returned to the user within a half hour.

Introduction

Economic disparities among people is a fact of life, and indeed often lies at the heart of major changes in society. The origin of this paper came from a life-long interest by the senior author, AK, in the seismic changes of society due to these economic inequalities, especially the inequality emerging today in the United States [1]. The popular term for the ultra-rich is the ‘1%’ [2]. Again and again, as the concentration of wealth increases, we end up seeing the somewhat discouraging charts showing how truly wealthy the 1% or more realistically the simply rich and ultra rich own, compared to everyone else. And, to make things worse, the stories become ever more heartbreaking as the conspicuous consumption of the ultra-rich is flaunted in the face of other people in lower socio-economic strata who have seen their lives damaged by inflation, their hopes for better economic lives buried under the insistent reduction of what they can afford and even what they can realistically attain. With this mournful issue stated, the notion was to understand the potential of artificial intelligence to help understand the mind-sets of people. Previous work by Moskowitz et. al., on the ‘fraying of America,’ looking at the responses of American a decade ago to different aspects of society suggested that people were not of one mind-set, but rather people differed among themselves in how they felt about the economic disparities. A decade later, with the foundation laid by the earlier work, two of the authors (AK and HM) decided to apply new tools afforded by AI to the mind of people presented with the story of the United States and its discouraging economic disparities.

The paper presented here is based upon the use of AI in an emerging science known as Mind Genomics [3,4]. Mind Genomics emerged as a way to understand the way people make decisions about the everyday. Rather than looking for general principles of behavior, principles which required artificial situations manipulated by an experimenter, Mind Genomics attempted to understand decision making by presenting people with vignettes, combinations of messages presenting ordinary acts of behavior in a topic area, such as voting (REF), or banking (REF), or even eating meals in a restaurant [5]. The output of these simple experiments was the ability to put numbers of different aspects of behavior to show how these aspects drove decisions, and then divide actual people into new to the world groups, mind-sets, based upon the similarities of the patterns of behavior that they evidenced for this simple, everyday task. These mind-sets often transcended countries, although the distribution of the same mind-set differed from country to country [6]. The discovery of mind-sets became a focus of many Mind Genomics studies. The approach was easy, the respondents had no problem evaluating different combinations of messages, and from the pattern of their ratings statistics had no problems first identifying the driving powers of the different messages, and then discovering the existence of meaningful mind-sets, groups of people with different ways of responding to the same messages.

The Introduction of AI to Mind Genomic Using Idea Coach, and Its Expansion to Scenarios

The underlying objective of the Mind Genomics process was that the user would achieve more knowledge by working at the level of the granular, rather than asking high level questions. The world of consumer research was overrun with surveys, most of which wanted top-line opinions of general topics, such as one’s opinion of the wait time in a doctor’s office, or the ease of purchasing a product, and the demeanor and sales behavior of the staff in a store. These general surveys ended up producing a great deal of score-card information about products and services, emerging indices like the Press Ganey score for medical services, and similar types of numbers [7]. What was not being provided from these scores was a deeper understanding of the experienced reality of the topic. The Mind Genomics effort emerged as a tool to understand the granular experience. All the users had to do was create combinations of messages, so called vignettes which were combinations of elements. The respondent would be exposed the vignettes, evaluate each vignette, and from the evaluation statistics such as regression and clustering would end up showing the strength of the elements (driving force) and mind-sets, as was noted above. There was only one issue which stopped Mind Genomics in its tracks in many cases. That was the fact that many prospective users ‘froze’ at the requirement to create questions which tell a story, and from those questions create four answers to each question. Answers were fairly easy to create, but questions were another thing entirely. Many users froze at the prospect of coming up with these questions, not realizing that the entire purpose of the questions was to create a structure bye which anyone could develop the necessary messages or elements to be used. By forcing the user to create the questions first, it was assumed that the user would be able to create the story through the questions, and then have little trouble providing the answers. The reality was that the prospect of having to ask questions and then to provide answers to those questions turned out to be more intimidating than one might have thought. The solution was not the extensive ‘training’ to teach people how to think, an effort which occasionally worked but more often than not ended in dismal failure. Rather, the solution emerged with the popular AI program, ChatGPT [8]. The Mind Genomics program, www.bimileap.com, was outfitted with the Idea Coach. Users could write in the topic, and in 15 seconds or so receive suggestions in the form of 15 questions. The process could be iterated ad infinitum, each time with AI returning its set of 15 questions, many new, some repeats. A further benefit was that the user could simply press a button to iterate to the next effort to create 15 questions, or if desired edit the prompt, called a ‘squib’ by Mind Genomics, the request, and see what emerged with the modified input [9]. Figure 1 shows the process. Panel A shows the request for four questions, the step which intimidated. Panel B shows the screen shot with Idea Coach read to receive input from the user. Panel C shows the input provided to Idea Coach by the user. Finally, Panel D shows the first part of the output provided by Idea Coach. Scrolling down revealed the full set of 15 suggested questions. The user simply needed to select a question and the question was automatically added to Panel A. the user could edit the question as well The process was rapid, simple, low cost, creating in its wake a simple system to run these Mind Genomics studies. As a benefit, the program kept a record of these iterations, and when the study set up was complete, the program sent the user the results of iterations, along with AI summarization of the results of the iterations. The process thus became a combination of problem solving and education, one available to every researcher.

Figure 1 shows the set-up process powered by AI embedded in Idea Coach. Panel A shows the request for four questions. Panel B shows the Idea Coach screen, with the box ready for input. The input could be far bigger than shown by the box. Panel C shows the same Idea Coach screen, this time with the box filled with input. Panel D shows the part of the output. The bottom of Panel D shows the steps that the suer can take. The top bar shows the, the opportunity to select a question ad drop it into the set of questions (as well as edit in on the fly). The middle bar show the opportunity to repeat the action without making any changes, which returned the new suggestions in 15-30 seconds. The bottom bar brings the user back to the Idea Coach input, allowing the user to edit the idea Coach input ‘on the fly,’ and then instantly rerun the request.

Figure 1: The Mind Genomics set-up screens under control of the user, with the help of Idea Coach, powered by AI.

Putting AI in Idea Coach to the Test: Providing Situations and Requesting Considered ‘Thinking’

The remainder of this paper focuses on the expansion of AI in the Mind Genomics process, not so much to provide questions and answers for study with people as with the desire to see whether Idea Coach could act more completely, almost as a person. The notion of working with AI to study scenarios is one of the attractions of AI. The objective of Mind Genomics was and remains to weave a coherent story about how people think by presenting them with combination of ideas, and measure how strong each of the ideas was to the person participating in the Mind Genomics study. The fortuitous event leading to this paper and to its companion effort was the it was possible to move Idea Coach into a higher level of functioning, simply by presenting the Idea Coach with a structured request. For this study, the nature of the information leading to request is shown in the top half of Table 1, the section titled: What is posited to be the case today (either by specific statements, or by general attitudes). The set of statements were developed by Arthur Kover, the senior author, as part of his analysis of the current situation in the United States. The actual request itself made to Idea Coach appears in the lower half of Table 2, the section entitled What AI is instructed to do based upon assumption of eight mind-sets. The important thing to keep in mind is the extensive set-up information at the start, and the far more structured request to AI as the follow on.

Table 1: Input to Idea Coach. Top panel = statement of current conditions, presented by user to AI as ‘facts. Bottom panel = request to AI from user.

Table 2: The eight mind-sets generated by AI in Idea Coach, based upon the information fed to it by the user (see Table 1, Top panel).

Positing the Mind-sets and Discussing the Way They Think

The first outcome from the AI embedded in the Idea Coach is the list of the mind-sets. Table 2 shows the list, along with the answers to the eight questions. AI provided full answers for seven of the eight ‘suggested mind-sets.’ AI did not provide anything about the eighth mind-set, other than the name ‘alternative community.’ The important things to understand from Table 2 is the depth of information and presumed insight proffered by the AI, when put on a specific question. To be sure, the orientation in Table 1 suggested the number eight for these mind-sets in the instructions to AI. Yet, one cannot fail to be impressed that the AI ‘fleshed out’ these simple statements, providing a context that would be accepted by a researcher, a news reporter, and even a novelist. The information provided by AI, or perhaps ‘spun up’ by AI seems realistic, internally consistent, and quite similar to what a person would say. The implications of this first part of the foray into the topic of today’s issue, the disparity between rich and poor, is the potential of creating a similar set of mind-sets for the dozens or even hundreds of topics facing society, first doing the work as a general topic, and then perhaps adding additional specifications, such as region of the world, period of history, and so forth. Those advances have to wait a little longer, although the speed of AI to answer these questions make it possible to create this large database of expected mind in a reasonably short period of time. Beyond the eight names of mind-sets is the ability of AI to provide answers which seem reasonable at first glance. Sometimes the AI failed to do exactly what the request to Idea Coach specified, such as giving the precise number of sentences or ideas requested, but for the most part the AI delivered what the user requested. Once again, the language and meaning of what was delivered makes intuitive sense, although successive iterations produced different words for the same questions. The variation from iteration to iteration suggests that the AI was using different sets of materials each time, although most likely materials from the same general set. Were there to be enough time and interest, one could run the same request 10 or more time, to see just how much variation in language and tonality would emerge across the 10 iterations. That is a potential topic for future work, to get a sense of the range of material and meanings delivered by AI across different efforts albeit for identical question.

After the user has completed the study set-up the Mind Genomics platform, www.bimileap, returns with summarizations of the idea which had emerged from the initial efforts, viz., questions and answers appearing in Table 1 (background materials), and Table 2 (questions and answers emerging from AI). In a sense Table 3 and successive ‘summarization’ tables provide new knowledge developed by AI.

Table 3 shows three different summaries:

Key ideas from the material provided
Themes emerging from the materials generated by AI
Perspectives on the themes (viz., commentary or new knowledge emerging from further analysis of the themes)

It becomes increasingly clear from this table that the incorporation of AI analyses into the project moves the statement of current conditions (Top panel of Table 1) into a far more profound ‘exegesis’ of the topic. Once again, Table 3 provides the input to help critical thinking about the topic.

Table 3: Summarization of key ideas, themes, and perspectives regarding the themes

The next step is technically not a summarization of the material, but rather the question of who would be positive about this material (Interested Audiences) versus who would be negative about this material (Opposing Audiences). Table 4 shows these two audiences.

Table 4: Relevant aspects of the issue for Interested Audiences versus for Opposing Audiences

A key benefit of the AI embedded in Idea Coach comes from the ability to identify new aspects, hitherto either unknown or perhaps not particularly well identified. Table 5 shows the final set of summaries. The first summary shows Alternative Viewpoints, which comprise a set of 15 questions presenting new ways of thinking about the topic. The second summary shows What’s Missing, comprising 12 direct questions. The third summary shows suggestions for innovations. This third summary on innovation looks at the topics, recasts the issue in terms new mind-sets for each new idea, and then presents the innovation, and how it will affect each of the newly minted mind-sets. Once again the summarization is new knowledge or at least conjecture, created by AI.

Table 5: New perspectives and suggestions emerging from AI

Creating Relevant Slogans for the Presidential Election Using the Mind-sets Generated by AI in Idea Coach

The final product of this experiment appears in Table 6. The product, in the second panel, comprises one slogan for each of the eight mind-sets. Each column corresponds to one mind-set. Each row corresponds to a hypothetical period of time (e.g., 2024, 1892, 1860) to a hypothesized person (e.g., Total US citizen, registered Democrat, registered Republican, registered Independent), and to a statement of economic condition.

Table 6: Slogans developed by AI, based upon instructions to Idea Coach

Discussion and Conclusions

The original thrust of this paper was to explore whether or not artificial intelligence could be used in the world of social policy [10,11], especially to enhance creativity in an area where opinions and power dominate [12]. The topic of AI and social policy has become exceptionally complex, often with the explorations shown in this paper overshadowed by the ethical issues [13,14]. The sheer fact of incorporating machines into society in a way which has the flavor of ‘control’ brings up the shades of George Orwell’s 1984 [15], and predecessor writings of this ilk [16]. The discussions of AI and ‘social research’ focuses primarily on ethics rather than on advances [17,18]. The range and depth of the discussions on AI do not so much focus on specifics but rather on discomfort, fears, which end up showing themselves as seemingly never-end discussions of ethical implications at the level of generality, rather than of specific issues. It may well be that the real contribution of AI to society and to governance have yet to be made, in contrast to the contribution of AI to applications, such as medicine [19], library work in the law [20], etc. It also may be the case that there is justification for the application of AI in situations of power of people over people, with AI giving an advantage in the ‘battle of all against all,’ the perfect description of the Hobbesian nightmare. Such nightmares would not be the case for the benign and often useful application of AI to solve humanity’s problems, rather than the fear of AI which, instead, could create these problems [21,22]. The paper just presented is one in a series of papers exploring the potential of artificial intelligence to promote critical thinking. The issue of critical thinking in the academic world is well covered in numerous publications [23]. There are methods for teaching critical thinking at a young age. The emphasis on critical thinking is to get the student to delve more deeply into a topic, develop hypotheses, test these hypotheses by one or another fashion [24,25]. One of the continuing issues in today’s world is how to make students focus on topics, especially in a world where their attention wanders from the topic or teacher to the little screen, their phone or tablet, where they can be entertained. Is it possible to teach critical thinking within that environment? The study or simulation study reported here may be one way to teach critical thinking about a topic. The issue here is positing and understanding mind-sets, and then creating slogans. The topic is serious, but at another level it is fun, not particularly challenging, returns with interesting results, and most important, returns with results which are real, and which themselves make a major contribution. It would be premature as well as difficult to enumerate the applications of the approach presented here. Within just a few minutes one could take a topic, provide one’s information of any type, instruct the AI to assumed mind-sets without saying what these mind-sets are, and then instruct the mind-sets to answer specific questions. At the surface level the answers make intuitive sense, perhaps because they are not presents facts but trends. It may be at this level, trends, generalities which make intuitive sense, that the approach presented here may enjoy its earliest success. Be that as it may, there may also be a way of presenting the AI with the right question so that the process takes place sometime in the well-defined past, or even sometime in the future. In those cases, one would assume that the AI uses the available information to extrapolate to the new condition. That extension remains for future work, but the effort, cost, speed, and simplicity all argue for an interest ‘next step’ along those lines. The ingoing vision of this study was to understand AI and politics, especially elections. The exiting vision is of a tool in the hands of billions of students, all of whom have fun playing with a system which educates them, makes them prospective experts in a topic with very little difficulty, almost overnight, and for very low cost. To close this paper, it is use to think of the thrust of which this paper is simply the latest. The paper is one of a series of papers exploring the potential of artificial intelligence to promote critical thinking. The issue of critical thinking in the academic world is well covered in numerous publications. The emphasis on critical thinking is to get the student to delve more deeply into a topic, develop hypotheses, test these hypotheses by one or another fashion [26,27]. In the end, it will be critical thinking, hand in hand with the increasing power of AI, which will see the proper use of technology to make a better world, not a world of machines and power-hungry individuals in control.

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