Energy-, time-, and labor-saving synthesis of α-ketiminophosphonates: Machine-learning-assisted simultaneous multiparameter screening for electrochemical oxidation

Masaru Kondo, Akimasa Sugizaki, Md Imrul Khalid, H. D.P. Wathsala, Kazunori Ishikawa, Satoshi Hara, Takayuki Takaai, Takashi Washio, Shinobu Takizawa, Hiroaki Sasai

Research output: Contribution to journalArticlepeer-review

25 Citations (Scopus)

Abstract

A highly efficient synthesis of α-ketiminophosphonates has been established for the electrochemical oxidation of α-amino phosphonates with the utilization of machine-learning-assisted simultaneous multiparameter screening. After brief experimental screening, the Bayesian optimization with the experimental data (up to 12 entries) could rapidly predict the optimal conditions for the synthesis of α-ketiminophosphonates and sulfonyl ketimines with aryl and alkyl groups. The obtained α-ketiminophosphonates could be converted into highly functionalized α-amino acid analogues with a tetrasubstituted carbon center.

Original languageEnglish
Pages (from-to)5825-5831
Number of pages7
JournalGreen Chemistry
Volume23
Issue number16
DOIs
Publication statusPublished - Aug 21 2021
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Environmental Chemistry
  • Pollution

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