Interpretation of stochastic electrochemical data

• DOI: 10.1016/j.coelec.2024.101505
• Publication Date: 2024-04-22T09:37:00Z
• Authors: Sina S. Jamali; Yanfang Wu; Axel M. Homborg; Serge G. Lemay; J. Justin Gooding
• Keywords: anzsrc-for: 40 Engineering; Physical chemistry; 34 Chemical Sciences; 3401 Analytical Chemistry; anzsrc-for: 3406 Physical chemistry; Nanotechnology; Bioengineering; anzsrc-for: 3401 Analytical Chemistry; anzsrc-for: 4018 Nanotechnology; Analytical chemistry; anzsrc-for: 34 Chemical Sciences; 40 Engineering; 620

Stochastic electrochemical measurement has come of age as a powerful analytical tool in corrosion science, electrophysiology, and single-entity electrochemistry. It relies on the fundamental trait that most electrochemical processes are stochastic and discrete in nature. Stochastic measurement of a single entity probes the charge transfer from a few or even one electroactive species. In corrosion, the stochastic measurements capture either the average amplitude/frequency of many events taking place spontaneously or probe discrete transients, signifying localized dissolution. The measurement principles vary in corrosion, single-entity, and electrophysiology, yet the main quantifiable values are commonly the frequency and amplitude of events. This perspective delves into the methodologies for the analysis and deconvolution of stochastic signals in electrochemistry. Ranging from visual assessment of transients to time/frequency analyses of the data and state-of-the-art machine learning, these methodologies mainly aim at identifying patterns, singular events, and rates of electrochemical processes from stochastic signals.