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Tailoring Hydrophobicity and Pore Environment in Physisorbents for Improved Carbon Dioxide Capture under High Humidity.

Xiaoliang WangMaytham AlzayerArthur J ShihSaptasree BoseHaomiao XieSimon M VornholtChristos D MalliakasHussain AlhashemFaramarz JoodakiSammer MarzoukGrace XiongMark Del CampoPierre Le MagueresFilip FormalikDebabrata SenguptaKaram B IdreesKaikai MaYongwei ChenKent O KirlikovaliTimur IslamogluKarena W ChapmanRandall Q SnurrOmar K Farha
Published in: Journal of the American Chemical Society (2024)
CALF-20, a Zn-triazolate-based metal-organic framework (MOF), is one of the most promising adsorbent materials for CO 2 capture. However, competitive adsorption of water severely limits its performance when the relative humidity (RH) exceeds 40%, limiting the potential implementation of CALF-20 in practical settings where CO 2 is saturated with moisture, such as postcombustion flue gas. In this work, three newly designed MOFs related to CALF-20, denoted as NU-220, CALF-20M-w, and CALF-20M-e that feature hydrophobic methyltriazolate linkers, are presented. Inclusion of methyl groups in the linker is proposed as a strategy to improve the uptake of CO 2 in the presence of water. Notably, both CALF-20M-w and CALF-20M-e retain over 20% of their initial CO 2 capture efficiency at 70% RH─a threshold at which CALF-20 shows negligible CO 2 uptake. Grand canonical Monte Carlo simulations reveal that the methyl group hinders water network formation in the pores of CALF-20M-w and CALF-20M-e and enhances their CO 2 selectivity over N 2 in the presence of a high moisture content. Moreover, calculated radial distribution functions indicate that introducing the methyl group into the triazolate linker increases the distance between water molecules and Zn coordination bonds, offering insights into the origin of the enhanced moisture stability observed for CALF-20M-w and CALF-20M-e relative to CALF-20. Overall, this straightforward design strategy has afforded more robust sorbents that can potentially meet the challenge of effectively capturing CO 2 in practical industrial applications.
Keyphrases
  • metal organic framework
  • carbon dioxide
  • healthcare
  • primary care
  • heavy metals
  • risk assessment
  • machine learning
  • quality improvement
  • room temperature
  • deep learning
  • human health