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Unveiling Competitive Adsorption in TiO₂ Photocatalysis through Machine-Learning-Accelerated Molecular Dynamics, DFT, and Experimental Methods.

ABSTRACT: The efficient harnessing of solar power for water treatment via photocatalytic processes has long been constrained by the challenge of understanding and optimizing the interactions at the photocatalyst surface, particularly in the presence of nontarget cosolutes. The adsorption of these cosolutes, such as natural organic matter, onto photocatalysts can inhibit the degradation of pollutants, drastically decreasing the photocatalytic efficiency. In the present work, computational methods are employed to predict the inhibitory action of a suite of small organic molecules during TiO₂ photocatalytic degradation of para-chlorobenzoic acid (pCBA). Specifically, tryptophan, coniferyl alcohol, succinic acid, gallic acid, and trimesic acid were selected as interfering agents

SUBMITTER: Allam O

PROVIDER: S-EPMC11261558 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature

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Unveiling Competitive Adsorption in TiO₂ Photocatalysis through Machine-Learning-Accelerated Molecular Dynamics, DFT, and Experimental Methods.

OmicsDI is part of the ELIXIR infrastructure

Tweets

Dataset Information

Unveiling Competitive Adsorption in TiO2 Photocatalysis through Machine-Learning-Accelerated Molecular Dynamics, DFT, and Experimental Methods.

OmicsDI is part of the ELIXIR infrastructure

Tweets

Unveiling Competitive Adsorption in TiO₂ Photocatalysis through Machine-Learning-Accelerated Molecular Dynamics, DFT, and Experimental Methods.