Electrochemical Carbon-Ferrier Rearrangement Using a Microflow Reactor and Machine Learning-Assisted Exploration of Suitable Conditions
Eisuke F. Sato, Gaku Tachiwaki, Mayu Fujii, Koichi Mitsudo, Takashi Washio, Shinobu Takizawa, Seiji Suga
Abstract
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.
Topics & Concepts
ElectrolysisElectrochemistryAnodeKrigingFlow chemistryProcess engineeringFlow (mathematics)Process (computing)Computer scienceChemistryCatalysisElectrodeEngineeringOrganic chemistryMechanicsMachine learningPhysicsOperating systemElectrolytePhysical chemistryMachine Learning in Materials ScienceInnovative Microfluidic and Catalytic Techniques InnovationElectrochemical Analysis and Applications