Direct characterization of gas adsorption and phase transition of a metal organic framework using in-situ Raman spectroscopy

CHEMICAL ENGINEERING JOURNAL(2023)

引用 1|浏览5
暂无评分
摘要
Adsorbents are widely used in gas separation and storage processes. Performance improvements are largely achieved through the continual development of new materials with unique sorption properties. Adsorption characterization techniques, therefore, play a central role in material research and development. Here, in-situ Raman spectroscopy is presented as a multi-purpose laboratory tool for analyzing adsorption performance. In contrast to conventional laboratory techniques requiring macroscopic samples, adsorption analysis via Raman spectroscopy can be performed on samples of less than 1 mg. Furthermore, simultaneous Raman multi-phase measurements of the adsorbent structure as well as the free and bound adsorbate, are shown to provide molecular insights into the operation of functional adsorbents at conditions representative of industrial applications, which are often not attainable in conventional crystallography. Firstly, a Raman-based method is demonstrated for directly quantifying absolute adsorption capacity within individual particles. The technique is validated for Raman measurements of carbon dioxide on silica gel and compared to gravimetric and volumetric analyses. Secondly, Raman spectroscopy is applied to study a novel functional material, ZIF-7, and directly probe its pressure-regulated gate-opening mechanism, which was only observed through indirect means. These Raman measurements confirm that the sharp increase in capacity corresponds to a structural transition in the material and reveal that multiple adsorption sites contribute to the overall capacity. The Raman methods presented here can be applied to a wide range of adsorbent-adsorbate systems and present a basis for further studies into the kinetics of sorption processes.
更多
查看译文
关键词
Raman spectroscopy,Adsorption,Adsorption capacity,Functionalized materials,Metal organic frameworks
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要