Computer vision for understanding catalyst degradation kinetics

Chunhui Yan, Megan Cowie, Calum Howcutt, Katherine Wheelhouse, Neil Hodnett, Martin Kollie, Martin Gildea, Martin H. Goodfellow, Marc Reid

Research output: Working paper

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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.
Original languageEnglish
Place of PublicationCambridge
Number of pages12
DOIs
Publication statusSubmitted - 10 Jun 2022

Keywords

  • computer vision
  • catalysis
  • kinetics
  • reaction monitoring
  • borylation
  • palladium
  • machine learning

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