An Algebraic Blueprint for Predicting Turnover Numbers and Endpoints in Photocatalysis

Photocatalysis is a contemporary research field given that the world's fossil energy resources including coal, mineral oil and natural gas are finite. The vast variety of photocatalytic systems demands for standardized protocols facilitating an objective comparison. While there are commonly acc...

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Bibliographic Details
Published in:Chemphyschem 2024-02, Vol.25 (3), p.e202300767-n/a
Main Authors: Klingler, Sarah, Hlavatsch, Michael, Bagemihl, Benedikt, Mengele, Alexander K., Gaus, Anna‐Laurine, Delius, Max, Rau, Sven, Mizaikoff, Boris
Format: Article
Language:English
Subjects:
Algebra
Analytical Method
Boundary conditions
Comparability
Data points
Energy sources
Impact analysis
Least squares method
Mineral oils
Natural gas
Performance Indicators
Photocatalysis
Turnover Number
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Summary:Photocatalysis is a contemporary research field given that the world's fossil energy resources including coal, mineral oil and natural gas are finite. The vast variety of photocatalytic systems demands for standardized protocols facilitating an objective comparison. While there are commonly accepted performance indicators such as the turnover number (TON) that are usually reported, to date there is no unified concept for the determination of TONs and the endpoint of the reaction during continuous measurements. Herein, we propose an algebraic approach using defined parameters and boundary conditions based on partial‐least squares regression for generically calculating and predicting the turnover number and the endpoint of a photocatalytic experiment. Furthermore, the impact of the analysis period was evaluated with respect to the fidelity of the obtained TON, and the influence of the data point density along critical segments of the obtained fitting function is demonstrated. We report an algebraic approach for calculating and predicting turnover number and endpoint of photocatalytic experiments based on the definition of boundary conditions using partial‐least squares regression. Thereby comparability in photocatalytic research can be significantly enhanced and the measurement effort and use of resources is substantially reduced.
ISSN:1439-4235
1439-7641
DOI:10.1002/cphc.202300767