Adsorption and Photocatalytic Activity of Covalent Organic Frameworks for Environmental Remediation: A Review of Density Functional Theory Studies

Molecules, 2026

Full text Open access: Gold Metadata (CSL-JSON)
DOI 10.3390/molecules31203695
Data confidence 85 high
How this score is calculated

It is built from checkable properties of the record itself: whether the article has a DOI, how many independent sources confirmed its data, whether the required fields are filled in, and how recent the latest confirmation is. Disagreement between sources and a missing identifier lower it. A value of 80 or above means the record was published without further checking; 60 to 79 means it is published at reduced confidence, with a manual check advisable. A record verified by an editor by hand is never pushed below 90 by a recalculation. This scores our data about the article, not the article itself.

Abstract

The contamination of water resources by heavy metals, radionuclides, and organic pollutants demands efficient materials for water treatment. Covalent organic frameworks (COFs) are promising candidates owing to their tunable porosity and functionalizable frameworks. This review examines density functional theory (DFT) studies of COFs for environmental remediation, distinguishing three frequently conflated processes: adsorptive capture, photocatalytic transformation, and combined adsorption–photocatalysis. Studies are analyzed across heavy metals, radionuclides, and organic pollutants, including antibiotics, pharmaceuticals, per- and polyfluoroalkyl substances (PFASs), dyes, pesticides, flame retardants, and others. Photocatalytic applications, including hydrogen peroxide (H2O2) production and others, are treated separately in terms of band alignment, charge separation, and reaction free energies. This review also assesses methodological limits of current DFT practice, including the incomparability of adsorption energies across functionals and model types, the distinction between adsorption energy and adsorption free energy, and the gap between computed energetics and experimental capacities.