Computer Vision for Understanding Catalyst Degradation Kinetics

10 June 2022, Version 1
This content is a preprint and has not undergone peer review at the time of posting.

Abstract

We report a computer vision strategy for the extraction and colorimetric analysis of catalyst degradation and product formation kinetics from video footage. The degradation of palladium(II) pre-catalyst systems to form ‘Pd black’ is investigated as a widely relevant case study for catalysis and materials chemistries. Beyond the study of catalysts in isolation, investigation of Pd-catalyzed Miyaura borylation reactions revealed informative correlations between colour parameters (most notably ΔE, a colour-agnostic measure of contrast change) and the concentration of product measured by off-line analysis (NMR and LC-MS). The breakdown of such correlations helped inform conditions under which reaction vessels were compromised by air ingress. These findings present opportunities to expand the toolbox of non-invasive analytical techniques, operationally cheaper and simpler to implement than common spectroscopic methods. The approach introduces the capability of analyzing the macroscopic ‘bulk’ for the study of reaction kinetics in complex mixtures, in complement to the more common study of microscopic and molecular specifics.

Keywords

computer vision
catalysis
kinetics
reaction monitoring
borylation
palladium
machine learning

Supplementary materials

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Description
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HPLC Traces
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Chromatogram printouts and associated methods
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LC-MS Traces
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Chromatogram printouts and associated methods.
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Supporting Information
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Main SI file, containing details of synthetic, computational, and statistical methods.
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