Electro-Descriptors for the Performance Prediction of Electro-Organic Synthesis

Yuxuan Chen, Bailin Tian, Zheng Cheng, Xiaoshan Li, Min Huang, Yuxia Sun, Shuai Liu, Xu Cheng, Shuhua Li*, Mengning Ding*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

36 Citations (Scopus)

Abstract

Electrochemical organic synthesis has attracted increasing attentions as a sustainable and versatile synthetic platform. Quantitative assessment of the electro-organic reactions, including reaction thermodynamics, electro-kinetics, and coupled chemical processes, can lead to effective analytical tool to guide their future design. Herein, we demonstrate that electrochemical parameters such as onset potential, Tafel slope, and effective voltage can be utilized as electro-descriptors for the evaluation of reaction conditions and prediction of reactivities (yields). An “electro-descriptor-diagram” is generated, where reactive and non-reactive conditions/substances show distinct boundary. Successful predictions of reaction outcomes have been demonstrated using electro-descriptor diagram, or from machine learning algorithms with experimentally-derived electro-descriptors. This method represents a promising tool for data-acquisition, reaction prediction, mechanistic investigation, and high-throughput screening for general organic electro-synthesis.

Original languageEnglish
Pages (from-to)4199-4207
Number of pages9
JournalAngewandte Chemie - International Edition
Volume60
Issue number8
DOIs
Publication statusPublished - 19 Feb 2021
Externally publishedYes

Keywords

  • electro-descriptors
  • electrochemistry
  • electroorganic synthesis
  • machine learning
  • reaction prediction

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