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

Autor:	Mayu Fujii, Takeshi Washio, Shinobu Takizawa, Masaru Kondo, Kazunori Ishikawa, Hiroki Tanaka, Koichi Mitsudo, Hiroaki Sasai, Seiji Suga, Eisuke Sato
Rok vydání:	2021
Předmět:	Reaction conditions Chemistry business.industry Scale (chemistry) Organic Chemistry Machine learning computer.software_genre Electrochemistry chemistry.chemical_compound Yield (chemistry) Carbon source Artificial intelligence Trimethylsilyl cyanide business computer
Zdroj:	The Journal of Organic Chemistry. 86:16035-16044
ISSN:	1520-6904 0022-3263
DOI:	10.1021/acs.joc.1c01242
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::55680401b084fbdfed436816734ffef0 https://doi.org/10.1021/acs.joc.1c01242 Zobrazit plný text záznamu