TY - JOUR
T1 - Application of an Electrochemical Microflow Reactor for Cyanosilylation
T2 - Machine Learning-Assisted Exploration of Suitable Reaction Conditions for Semi-Large-Scale Synthesis
AU - Sato, Eisuke
AU - Fujii, Mayu
AU - Tanaka, Hiroki
AU - Mitsudo, Koichi
AU - Kondo, Masaru
AU - Takizawa, Shinobu
AU - Sasai, Hiroaki
AU - Washio, Takeshi
AU - Ishikawa, Kazunori
AU - Suga, Seiji
N1 - Funding Information:
This work was supported in part by JSPS KAKENHI Grant Nos. JP19K05477, JP19K05478, JP20K22534, and JP18H04455 for Middle Molecular Strategy and JST CREST Grant No. JPMJCR18R1. We thank NIPPOH Chemicals Co., Ltd. for their gift of trimethylsilyl cyanide.
Publisher Copyright:
© 2021 American Chemical Society
PY - 2021/11/19
Y1 - 2021/11/19
N2 - 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.
AB - 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.
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U2 - 10.1021/acs.joc.1c01242
DO - 10.1021/acs.joc.1c01242
M3 - Article
AN - SCOPUS:85113886081
SN - 0022-3263
VL - 86
SP - 16035
EP - 16044
JO - Journal of Organic Chemistry
JF - Journal of Organic Chemistry
IS - 22
ER -