Use of Artificial Intelligence in Electrode Reaction Mechanism Studies: Predicting Voltammograms and Analyzing the Dissociative CE Reaction at a Hemispherical Electrode | Zendy

Haotian Chen | Zendy; Enno Kätelhön | Zendy; Haonan Le | Zendy; Richard G. Compton | Zendy

Open Access

Use of Artificial Intelligence in Electrode Reaction Mechanism Studies: Predicting Voltammograms and Analyzing the Dissociative CE Reaction at a Hemispherical Electrode

Author(s) -

Haotian Chen,

Enno Kätelhön,

Haonan Le,

Richard G. Compton

Publication year - 2021

Publication title -

analytical chemistry

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 2.117

H-Index - 332

eISSN - 1520-6882

pISSN - 0003-2700

DOI - 10.1021/acs.analchem.1c03154

Subject(s) - chemistry , electrode , reaction rate constant , dissociative , reaction mechanism , kinetic energy , standard electrode potential , diffusion , mechanism (biology) , voltammetry , analytical chemistry (journal) , electrochemistry , thermodynamics , kinetics , chromatography , catalysis , organic chemistry , medicine , philosophy , physics , quantum mechanics , epistemology , pharmacology

Artificial intelligence (AI) is used to learn the key voltammetric characteristics of the dissociative CE mechanism via training from multiple simulations using bespoke code. This allows first for the prediction of voltammograms without the need for further simulations, given knowledge of the relevant experimental parameters (rate and equilibrium constants, electrode geometry, and diffusion coefficients). Second, it is applied to analyze noisy experimental voltammetry to characterize the mechanistic type and to successfully extract the key kinetic and thermodynamic parameters.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research