JOURNAL ARTICLE
Exhaled Anesthetic Xenon Regeneration by Gas Separation Using a Metal–Organic Framework with Sorbent‐Sorbate Induced‐Fit.
Published In: Angewandte Chemie International Edition, 2024, v. 63, n. 38. P. 1 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Zhao, Li; Peng, Xiaowan; Deng, Chenghua; Li, Jia‐Han; Pan, Huiyuan; Zou, Jin‐Sheng; Liu, Bei; Deng, Chun; Xiao, Peng; Sun, Changyu; Peng, Yun‐Lei; Chen, Guangjin; Zaworotko, Michael J. 3 of 3
Abstract
Noble gas xenon (Xe) is an excellent anesthetic gas, but its rarity, high cost and constrained production prohibits wide use in medicine. Here, we have developed a closed‐circuit anesthetic Xe recovery and reusage process with highly effective CO2‐specific adsorbent CUPMOF‐5 that is promising to solve the anesthetic Xe supply problem. CUPMOF‐5 possesses spacious cage cavities interconnected in four directions by confinement throat apertures of ~3.4 Å, which makes it an ideal molecular sieving of CO2 from Xe, O2, N2 with the benchmark selectivity and high uptake capacity of CO2. In situ single‐crystal X‐ray diffraction (SCXRD) and computational simulation solidly revealed the vital sieving role of the confined throat and the sorbent‐sorbate induced‐fit strengthening binding interaction to CO2. CUPMOF‐5 can remove 5 % CO2 even from actual moist exhaled anesthetic gases, and achieves the highest Xe recovery rate (99.8 %) so far, as verified by breakthrough experiments. This endows CUPMOF‐5 great potential for the on‐line CO2 removal and Xe recovery from anesthetic closed‐circuits. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:Angewandte Chemie International Edition. 2024/09, Vol. 63, Issue 38, p1
- Document Type:Article
- Subject Area:Chemistry
- Publication Date:2024
- ISSN:1433-7851
- DOI:10.1002/anie.202407840
- Accession Number:179945787
- Copyright Statement:Copyright of Angewandte Chemie International Edition is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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