Electrochemical Mechanistic Analysis from Cyclic Voltammograms Based on Deep Learning.

For decades, employing cyclic voltammetry for mechanistic investigation has demanded manual inspection of voltammograms. Here, we report a deep-learning-based algorithm that automatically analyzes cyclic voltammograms and designates a probable electrochemical mechanism among five of the most common ones in homogeneous molecular electrochemistry. The reported algorithm will aid researchers' mechanistic analyses, utilize otherwise elusive features in voltammograms, and experimentally observe the gradual mechanism transitions encountered in electrochemistry.

Homogeneous molecular catalysis of the electrochemical reduction of NO to N: redox chemical catalysis.

Homogeneous electrochemical catalysis of NO reduction to N is investigated with a series of organic catalysts and rhenium and manganese bipyridyl carbonyl complexes. An activation-driving force correlation is revealed with the organic species characteristic of a redox catalysis involving an outer-sphere electron transfer from the radical anions or dianions of the reduced catalyst to NO. Taking into account the previously estimated reorganization energy required to form the NO radical anions leads to an estimation of the NO/NO˙ standard potential in acetonitrile electrolyte.