Evidence map›Paper›PMID 39878149›Full record

ArticleACS electrochemistry2025

Redox-Detecting Deep Learning for Mechanism Discernment in Cyclic Voltammograms of Multiple Redox Events.

Benjamin B Hoar, Weitong Zhang, Yuanzhou Chen, Jingwen Sun, Hongyuan Sheng, Yucheng Zhang, Yisi Chen, Jenny Y Yang, Cyrille Costentin, Quanquan Gu and 1 more

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Article in ACS electrochemistry, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Benjamin B HoarDepartment of Chemistry and Biochemistry, University of California Los Angeles, Los Angeles, California 90095, United States.
Weitong ZhangDepartment of Computer Science, University of California Los Angeles, Los Angeles, California 90095, United States.
Yuanzhou ChenDepartment of Computer Science, University of California Los Angeles, Los Angeles, California 90095, United States.
Jingwen SunDepartment of Chemistry and Biochemistry, University of California Los Angeles, Los Angeles, California 90095, United States.ORCID https://orcid.org/0000-0003-3657-7672
Hongyuan ShengDepartment of Chemistry and Biochemistry, University of California Los Angeles, Los Angeles, California 90095, United States.
Yucheng ZhangThe Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, Texas 78712, United States.
Yisi ChenDepartment of Chemistry and Biochemistry, University of California Los Angeles, Los Angeles, California 90095, United States.
Jenny Y YangDepartment of Chemistry, University California Irvine, Irvine, California 92697, United States.ORCID https://orcid.org/0000-0002-9680-8260
Cyrille CostentinUniversité Grenoble-Alpes, DCM, CNRS, 38000 Grenoble, France.ORCID https://orcid.org/0000-0002-7098-3132
Quanquan GuDepartment of Computer Science, University of California Los Angeles, Los Angeles, California 90095, United States.
Chong LiuDepartment of Chemistry and Biochemistry, University of California Los Angeles, Los Angeles, California 90095, United States.ORCID https://orcid.org/0000-0001-5546-3852

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In electrochemical analysis, mechanism assignment is fundamental to understanding the chemistry of a system. The detection and classification of electrochemical mechanisms in cyclic voltammetry set the foundation for subsequent quantitative evaluation and practical application, but are often based on relatively subjective visual analyses. Deep-learning (DL) techniques provide an alternative, automated means that can support experimentalists in mechanism assignment. Herein, we present a custom DL architecture dubbed as EchemNet, capable of assigning both voltage windows and mechanism classes to electrochemical events within cyclic voltammograms of multiple redox events. The developed technique detects over 96% of all electrochemical events in simulated test data and shows a classification accuracy of up to 97.2% on redox events with 8 known mechanisms. This newly developed DL model, the first of its kind, proves the feasibility of redox-event detection and electrochemical mechanism classification with minimal

Identifiers

PMID39878149
PMCPMC11728721

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.