Inclusion of multiple cycling of potential in the deep neural network classification of voltammetric reaction mechanisms-Reference-Cited by-同舟云学术

Inclusion of multiple cycling of potential in the deep neural network classification of voltammetric reaction mechanisms

Published:2022 Issue: Volume: Page:
ISSN:1359-6640
Container-title:Faraday Discussions
language:en
Short-container-title:Faraday Discuss.

Author:

Gundry Luke¹^ORCID,Kennedy Gareth¹,Bond Alan M.¹^ORCID,Zhang Jie¹^ORCID

Affiliation:

1. School of Chemistry, Monash University, Clayton, Vic. 3800, Australia

Abstract

Deep neural networks applied to three cycle voltammograms showed significant advantages in classifying difficult simulated E, EC1st and EC2nd processes.

Funder

Australian Research Council

Publisher

Royal Society of Chemistry (RSC)

Subject

Physical and Theoretical Chemistry

Link

http://pubs.rsc.org/en/content/articlepdf/2022/FD/D1FD00050K

Reference30 articles.

1. Classification and authentication of unknown water samples using machine learning algorithms

2. Automatically Identifying Electrode Reaction Mechanisms Using Deep Neural Networks

3. Machine Learning Techniques for Chemical Identification Using Cyclic Square Wave Voltammetry

4. Analyzing the anodic stripping square wave voltammetry of heavy metal ions via machine learning: Information beyond a single voltammetric peak

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