Electrochemical Carbon-Ferrier Rearrangement Using a Microflow Reactor and Machine Learning-Assisted Exploration of Suitable Conditions
Electrochemical Carbon-Ferrier Rearrangement Using a Microflow Reactor and Machine Learning-Assisted Exploration of Suitable Conditions
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DOI：
10.1021/acs.oprd.2c00267
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发表时间：
2023-01
期刊：
影响因子：
--
通讯作者：
Eisuke Sato;Gaku Tachiwaki;Mayu Fujii;K. Mitsudo;T. Washio;Shinobu Takizawa;Seiji Suga
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文献类型：
--
作者：
Eisuke Sato;Gaku Tachiwaki;Mayu Fujii;K. Mitsudo;T. Washio;Shinobu Takizawa;Seiji Suga
在电解中使用流动反应器可以实现高效和可扩展的合成，而这通常是间歇反应器难以完成的。我们实现了碳-铁的电化学重排，这种重排是通过催化阳极氧化进行的，这种转化可以使用电化学流动反应器进行。可以使用机器学习方法--高斯过程回归(GPR)来调整从流动反应器获得的附加数值参数。探地雷达可以构建两个模型来估计产量和生产率，并且可以合理地选择反应条件。
The use of a flow reactor in electrolysis enables efficient and scalable synthesis, which is normally difficult to accomplish by batch reactors. We achieved electrochemical carbon-Ferrier rearrangement which proceeded with catalytic anodic oxidation, and this transformation could be performed using an electrochemical flow reactor. Additional numeric parameters derived from the flow reactor could be adjusted using Gaussian process regression (GPR), which is a machine learning method. GPR enables the construction of two models to estimate yields and productivity, and the reaction condition can be selected rationally.