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

Abstract

Quantitative structure–property relationship models that correlate the water adsorption performance of MOFs to their physicochemical features have been established.

Minimal crystallographic descriptors of sorption properties in hypothetical MOFs and role in sequential learning optimization

“…To this end, we notice that Chaemchuen et al have reported H 2 O working capacity for a pool of 66 MOFs 5 . A good correlation between the water uptake and the surface area (i.e., the available internal surface per gram of dry adsorbent) can be observed for typical MOFs used in the energy engineering field, and this is also in line with results found by Xu et al 66 . In this work, on the basis of that correlation, we impose a linear regression for finding the constant of proportionality between water uptake and surface area (water uptake = η × surface area).…”

Improvement of water adsorption performance of UiO-66 by post-synthetic modification

“…The α of PSE samples decreased and K H increased by varying degrees, H-UiO-66-PyDC has the lowest α and the highest K H , showing the best hydrophilicity. Xu and co-workers 12 established a quantitative structure-property relationship model that can correlate α of MOFs to their pore size and atomic partial charge. The results indicate that when the atomic partial charge remains constant, a decrease in the pore size is beneficial for improving the hydrophilicity of MOFs.…”

“…In recent years, some MOFs have shown good application prospects in these tech-niques related to water adsorption processes due to their high water stability, high water adsorption capacity, and good water affinity. [9][10][11][12] MIL-160, COP-27 (Ni), and UiO-66 [13][14][15][16][17] are suitable for applications in adsorption heat storage or refrigeration. ZJNU-30 and MOF-801 18,19 exhibit excellent performance in atmospheric water collection.…”

Sorption Thermal Energy Storage Performance of Nanoporous Metal–Organic Frameworks and Covalent Organic Frameworks by Grand Canonical Monte Carlo Simulations

“…The question of how to choose a promising candidate from a vast number of adsorbent materials, evaluate their Q ed ’s, and extract the structure–property relationships for sorption thermal energy storage remains unanswered. High-throughput computational screening has been validated as a high-efficient approach to identifying potential sorbents for practical applications, such as carbon capture, , hydrogen storage, , and noble gas separation, , which have also been widely adopted in heat transformation applications . Our previous work developed a computational screening strategy to screen out the top-performing working pairs for adsorption heat pumps (AHPs) by calculating the coefficient of performance (COP) of MOFs and COFs using integrated Grand Canonical Monte Carlo (GCMC) simulations. , Subsequently, the screening of MOF–water, MOF–methanol, and MOF–ammonia working pairs was also performed by evaluating their COP, in which several high-performing candidates were identified.…”