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Connectivity Analysis of Adsorption Sites in Metal-Organic Frameworks for Facilitated Water Adsorption.

Zhi-Xun XuYi-Ming WangLi-Chiang Lin
Published in: ACS applied materials & interfaces (2023)
Metal-organic frameworks (MOFs) have recently drawn considerable attention as promising adsorbents to harvest atmospheric water. To achieve an efficient harvesting process, seeking MOFs that demonstrate sharp condensation behavior is the key. Given that the clustering of water molecules in MOFs should be driven by not only MOF-water interactions but also water-water interactions, the spatial arrangement of water adsorption sites in a MOF is therefore crucial. Specifically, this study demonstrates the critical role of continuous adsorption channels (CACs) in MOFs. Such CACs will enable water molecules to stay in proximity and in a continuous manner, thus promoting the formation of hydrogen bonds and, consequently, the clustering of water molecules. We have developed an automatic algorithm to detect CACs based on the energy grid of host-guest interactions and applied the algorithm to more than 2000 diverse structures. The results show that more than 80% of the studied MOFs displaying water condensation at 298 K and 20% relative humidity predicted by Monte Carlo simulations indeed have CACs. The developments herein are anticipated to largely facilitate the future discovery of optimal adsorbents for water harvesting or water-adsorption-related applications in general. A Python-based code for detecting CACs in porous materials is also provided along with this article to employ this approach.
Keyphrases
  • metal organic framework
  • machine learning
  • monte carlo
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