Dataset Information

Experimental Voltammetry Analyzed Using Artificial Intelligence: Thermodynamics and Kinetics of the Dissociation of Acetic Acid in Aqueous Solution.

ABSTRACT: Artificial intelligence (AI) is used to quantitatively analyze the voltammetry of the reduction of acetic acid in aqueous solution generating thermodynamic and kinetic data. Specifically, the variation of the steady-state current for the reduction of protons at a platinum microelectrode as a function of the bulk concentration of acetic acid is recorded and analyzed giving data in close agreement with independent measurements, provided the AI is trained with accurate and precise knowledge of diffusion coefficients of acetic acid, acetate ions, and H⁺.

SUBMITTER: Chen H

PROVIDER: S-EPMC9082489 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature

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Experimental Voltammetry Analyzed Using Artificial Intelligence: Thermodynamics and Kinetics of the Dissociation of Acetic Acid in Aqueous Solution.

Chen Haotian H Li Danlei D Kätelhön Enno E Miao Ruiyang R Compton Richard G RG

Analytical chemistry 20220405 15

Artificial intelligence (AI) is used to quantitatively analyze the voltammetry of the reduction of acetic acid in aqueous solution generating thermodynamic and kinetic data. Specifically, the variation of the steady-state current for the reduction of protons at a platinum microelectrode as a function of the bulk concentration of acetic acid is recorded and analyzed giving data in close agreement with independent measurements, provided the AI is trained with accurate and precise knowledge of diff ...[more]

PMID: 35381175

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Project description:BackgroundThe sublingual microcirculation presumably exhibits disease-specific changes in function and morphology. Algorithm-based quantification of functional microcirculatory hemodynamic variables in handheld vital microscopy (HVM) has recently allowed identification of hemodynamic alterations in the microcirculation associated with COVID-19. In the present study we hypothesized that supervised deep machine learning could be used to identify previously unknown microcirculatory alterations, and combination with algorithmically quantified functional variables increases the model's performance to differentiate critically ill COVID-19 patients from healthy volunteers.MethodsFour international, multi-central cohorts of critically ill COVID-19 patients and healthy volunteers (n = 59/n = 40) were used for neuronal network training and internal validation, alongside quantification of functional microcirculatory hemodynamic variables. Independent verification of the models was performed in a second cohort (n = 25/n = 33).ResultsSix thousand ninety-two image sequences in 157 individuals were included. Bootstrapped internal validation yielded AUROC(CI) for detection of COVID-19 status of 0.75 (0.69-0.79), 0.74 (0.69-0.79) and 0.84 (0.80-0.89) for the algorithm-based, deep learning-based and combined models. Individual model performance in external validation was 0.73 (0.71-0.76) and 0.61 (0.58-0.63). Combined neuronal network and algorithm-based identification yielded the highest externally validated AUROC of 0.75 (0.73-0.78) (P < 0.0001 versus internal validation and individual models).ConclusionsWe successfully trained a deep learning-based model to differentiate critically ill COVID-19 patients from heathy volunteers in sublingual HVM image sequences. Internally validated, deep learning was superior to the algorithmic approach. However, combining the deep learning method with an algorithm-based approach to quantify the functional state of the microcirculation markedly increased the sensitivity and specificity as compared to either approach alone, and enabled successful external validation of the identification of the presence of microcirculatory alterations associated with COVID-19 status.

Project description:The bromine-selenite reaction at strongly acidic conditions was investigated by monitoring the absorbance-time traces at the isosbestic point of the bromine-tribromide system at a constant ionic strength (0.5 M adjusted by sodium perchlorate) and temperature. Despite the simplicity of the stoichiometry, the kinetics was found to be very complex. Although the formal kinetic orders of the reactants bromine and selenite are strictly 1, that of the hydrogen ion varies from -2 to less than -3 and notably depends on the initial bromide concentration as well. The bromide ion also inhibits the reaction, making the whole system as a sound example of efficient autoinhibition. We have clearly shown that the inhibitory effect of the bromide ion cannot be explained quantitatively by either exclusively considering the unreactivity of the tribromide ion over elemental bromine or driving the reaction via hypobromous acid formed from the well-known hydrolysis of bromine in aqueous solutions. Instead of that, bromonium ion transfer initiating equilibrium is suggested between the selenium(IV) and bromine species to produce bromide ion and SeO3Br- followed by the hydrolysis of this short-lived intermediate. This hydrolytic transformation was found to be catalytic with respect to hydroxide and bromide ions as well. We have also demonstrated that, among the wide variety of selenium species present in the acidic aqueous solution, the best result can be obtained by considering HSeO3 - as the kinetically active species toward bromine. The proposed mechanism containing 10 acid-base equilibria with known equilibrium constants, the above-mentioned initiating equilibrium, and the hydrolysis of SeO3Br- is able to fit all 49 kinetic absorbance-traces simultaneously, taking into account properly the most important characteristics of the measured data at strongly acidic conditions. Furthermore, this kinetic model was further extended by the direct reactions of hypobromous acid with selenium(IV) species suggested previously with reasonably modified rate coefficients to describe the pH dependence of the apparent second-order rate coefficients over the pH = 1-13 range, providing a useful tool to predict more accurately the kinetic behavior of selenium(IV) species in water treatment process conditions.

Dataset Information

Experimental Voltammetry Analyzed Using Artificial Intelligence: Thermodynamics and Kinetics of the Dissociation of Acetic Acid in Aqueous Solution.

Publications

Experimental Voltammetry Analyzed Using Artificial Intelligence: Thermodynamics and Kinetics of the Dissociation of Acetic Acid in Aqueous Solution.

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