Application of an Electrochemical Microflow Reactor for Cyanosilylation: Machine Learning-Assisted Exploration of Suitable Reaction Conditions for Semi-Large-Scale Synthesis

Eisuke Sato, Mayu Fujii, Hiroki Tanaka, Koichi Mitsudo, Masaru Kondo, Shinobu Takizawa, Hiroaki Sasai, Takeshi Washio, Kazunori Ishikawa, Seiji Suga

Research output: Contribution to journalArticlepeer-review

Abstract

Cyanosilylation of carbonyl compounds provides protected cyanohydrins, which can be converted into many kinds of compounds such as amino alcohols, amides, esters, and carboxylic acids. In particular, the use of trimethylsilyl cyanide as the sole carbon source can avoid the need for more toxic inorganic cyanides. In this paper, we describe an electrochemically initiated cyanosilylation of carbonyl compounds and its application to a microflow reactor. Furthermore, to identify suitable reaction conditions, which reflect considerations beyond simply a high yield, we demonstrate machine learning-Assisted optimization. Machine learning can be used to adjust the current and flow rate at the same time and identify the conditions needed to achieve the best productivity.

Original languageEnglish
JournalJournal of Organic Chemistry
DOIs
Publication statusAccepted/In press - 2021

ASJC Scopus subject areas

  • Organic Chemistry

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