Machine-learning-guided design of electroanalytical pulse waveforms

2025/01/01 by Cameron S. Movassaghi, Katie A. Perrotta, Maya E. Curry +7 · 1 voice · 1 citation
Chemical Engineering · Chemistry · Engineering · #Analytical Chemistry and Sensors #Fault Detection and Control Systems #Spectroscopy and Chemometric Analyses

paper · pdf · doi:10.1039/d5dd00005j

openalex publication_date 2025/01/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/29

Abstract

Bayesian optimization outperforms random- and human-designed waveforms for electrochemical detection of serotonin in physiologically-relevant environments.

Citations

• Bayesian reaction optimization as a tool for chemical synthesis
• Tandem electrocatalytic N2 fixation via proton-coupled electron transfer
• Physics makes the difference: Bayesian optimization and active learning via augmented Gaussian process

Cited by

• In vivo multimodal neurochemical interfaces for real-time decoding of brain circuit

Discussions

• Can machine learning help us design better electrochemical sensors? Movassaghi, Perrotta & Andrews show how Bayesian optimization outperformed chemist-designed and randomly generated voltammetry wavef [bsky, 1 points, 0 comments]

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