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The use of metal-organic frameworks (MOFs) as solid adsorption materials for carbon capture is promising, but achieving efficient and reversible adsorption with a balance of capacity and selectivity for carbon dioxide (CO2) over N2 remains a challenge. To take full advantage of the strong channel traffic and robustness of MOFs with relatively small pores, it is highly necessary to employ a defect-engineering strategy to construct a broader channel structure that can facilitate the loading of functional motif-rich amino acids (AAs). This strategy can greatly enhance the CO2 adsorption performance of MOF. In this study, motif-rich amino acids are loaded into the defective and robust porous frameworks via combined defect-engineering and post-synthetic methods. The defective Zr/Hf-MOF-808s modified with AAs, especially for the 18 mol% 4-nitroisophthalic acid, generated defective products allowing for the loading of L-serine (L-Ser). This modification resulted in a significant improvement in both the adsorption capacity (248% improvement at 298 K, 100 kPa) and the selectivity of CO2/N2 using the ideal adsorbed solution theory (IAST), with the selectivity increasing to 120.55 and 38.27 at 15 and 100 kPa, respectively, while maintaining good cycling performance. Density functional theory (DFT) simulation, CO2 temperature-programmed desorption (CO2-TPD), and in situ Fourier transform infrared spectroscopy (FTIR) were further employed to have a better understanding of the enhanced CO2 adsorption capacity. Interestingly, unlike the AAs loaded pristine MOF-808s that showed the best CO2 adsorption capacity with the loading of short and small glycine (Gly), the broadened channel size in our work enables the loading of functional motif-rich L-serine, which brings more active binding sites, improving CO2 adsorption.

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Publication history
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Acknowledgements

Publication history

Received: 03 May 2023
Revised: 22 June 2023
Accepted: 26 June 2023
Published: 10 August 2023
Issue date: March 2024

Copyright

© Tsinghua University Press 2023

Acknowledgements

Acknowledgements

This work was financially supported by the National Natural Science Foundation of China (Nos. 52170119 and 22178357) and the Youth Innovation Promotion Association of the Chinese Academy of Sciences (No. 2021044).

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