Issue 57, 2020, Issue in Progress

Screening of metal–organic frameworks for water adsorption heat transformation using structure–property relationships

Abstract

It is of great importance to correlate the water adsorption performance of MOFs to their physicochemical features in order to design and prepare MOFs for applications in adsorption heat transformation. In this work, both data analysis from existing studies and Grand Canonical Monte Carlo molecular simulation investigations were carried out. The results indicated that the highest water adsorption capacity was determined by the pore volume of MOF adsorbents, while there was a linear correlation interrelationship between isosteric heats of adsorption and the water adsorption performance at a low relative pressure. More detailed analysis showed that the charge distribution framework and pore size of MOFs contributed together to the hydrophilicity. Electrostatic interaction between water molecules and the framework atoms played a key role at low relative water pressure. A quantitative structure–property relationship model that can correlate the hydrophilicity of MOFs to their pore size and atomic partial charge was established. Along with some qualitative considerations, the screening methodology is proposed and is used to screen proper MOFs in the CoRE database. Seven MOFs were detected, and four of them were synthesized to validate the screening principle. The results indicated that these four MOFs possessed outstanding water adsorption performance and could be considered as promising candidates in applications for adsorption heating and cooling.

Graphical abstract: Screening of metal–organic frameworks for water adsorption heat transformation using structure–property relationships

Supplementary files

Article information

Article type
Paper
Submitted
22 Jul 2020
Accepted
28 Aug 2020
First published
18 Sep 2020
This article is Open Access
Creative Commons BY-NC license

RSC Adv., 2020,10, 34621-34631

Screening of metal–organic frameworks for water adsorption heat transformation using structure–property relationships

M. Xu, Z. Liu, X. Huai, L. Lou and J. Guo, RSC Adv., 2020, 10, 34621 DOI: 10.1039/D0RA06363K

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