ArticleIndustrial & engineering chemistry research2023
A Robust Framework for Generating Adsorption Isotherms to Screen Materials for Carbon Capture.
Article in Industrial & engineering chemistry research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Fast process-level screening of metal-organic frameworks for adsorption separation using 3D classical density functional theory.Molecular systems design & engineering · 2026Article
- Efficient Prediction of Multicomponent Adsorption Isotherms and Enthalpies of Adsorption in MOFs Using Classical Density Functional Theory.The journal of physical chemistry. B · 2026Article
- Queryable Gaseous Adsorption Properties of Pure Components and Mixtures in Metal-Organic Frameworks.Langmuir : the ACS journal of surfaces and colloids · 2025Article
- An ecosystem of carbon dioxide removal reviews - part 1: direct air COEnergy & environmental science · 2025Review
- Adsorption Modeling Based on Classical Density Functional Theory and PC-SAFT: Temperature Extrapolation and Fluid Transfer.Industrial & engineering chemistry research · 2024Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
To rank the performance of materials for a given carbon capture process, we rely on pure component isotherms from which we predict the mixture isotherms. For screening a large number of materials, we also increasingly rely on isotherms predicted from molecular simulations. In particular, for such screening studies, it is important that the procedures to generate the data are accurate, reliable, and robust. In this work, we develop an efficient and automated workflow for a meticulous sampling of pure component isotherms. The workflow was tested on a set of metal-organic frameworks (MOFs) and proved to be reliable given different guest molecules. We show that the coupling of our workflow with the Clausius-Clapeyron relation saves CPU time, yet enables us to accurately predict pure component isotherms at the temperatures of interest, starting from a reference isotherm at a given temperature. We also show that one can accurately predict the CO
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Registered trials
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