Are Hemilabile Metal−Organic Frameworks Overlooked as Promising Water-Stable Adsorbents? Elucidating Their Physical and Hydrolytic Properties Using Machine-Learned Potentials

25 April 2025, Version 1
This content is a preprint and has not undergone peer review at the time of posting.

Abstract

Poor water stability of many metal–organic frameworks (MOFs) remains a persistent bottleneck toward their practical applications. Recently, hemilabile STAMs experimentally demonstrate much stronger water stability, as well as improved adsorption performance under humid conditions, than the compositionally similar HKUST-1. Yet, the fundamental properties of STAMs remain largely unexplored. Herein, we apply the density-functional theory (DFT) calculations and machine-learned potentials (MLP) based molecular dynamics (MD) simulations to evaluate the physical and hydrolytic properties of STAMs functionalized with various hydrophobic/hydrophilic groups. Encouragingly, STAMs are predicted to have high mechanical strength and low heat capacity, which are desirable attributes in adsorption processes. Using MLP-MD simulations, we reveal that the Cu-paddlewheels in STAMs are generally stable even at a high water loading, despite certain defects. Moreover, we elucidate the effect of tuning the hydrophobicity of functional groups on water stability and dynamics. Particularly, hydrophobic groups impede the formation of energetically stable water pentamer and water diffusion in the hexagonal pores of STAMs. This indicates that the adsorption selectivity of polar (hydrophilic) or nonpolar (hydrophobic) solvents in the hexagonal pores can be tailored by modifying the hydrophobicity of functional groups, while adsorption in the triangular pores remains unaffected. More generally, beyond evidencing that STAMs are promising adsorbents in carbon capture and water-harvesting applications, we provide microscopic insights into hydrolytic properties, water stability and dynamics in STAMs that can potentially facilitate the design of new water-stable MOFs.

Keywords

Metal–organic frameworks
Machine-Learned potentials

Comments

Comments are not moderated before they are posted, but they can be removed by the site moderators if they are found to be in contravention of our Commenting Policy [opens in a new tab] - please read this policy before you post. Comments should be used for scholarly discussion of the content in question. You can find more information about how to use the commenting feature here [opens in a new tab] .
This site is protected by reCAPTCHA and the Google Privacy Policy [opens in a new tab] and Terms of Service [opens in a new tab] apply.