A novel framework for Adsorption Thermodynamics: Combining standardized methodology with machine learning-based text classification

Shuangjun Li, Yuanming Li, Shuai Deng, Xiangkun Elvis Cao and Ki Bong Lee

Energy, 2025, vol. 329, issue C

Abstract: Adsorption is a common surface phenomenon that involves the transfer of adsorbates onto solid adsorbent surfaces. It allows for calculating changes in thermodynamic variables, revealing their fundamental nature. There is still a lack of clear definitions and unified standards for thermodynamic systems in Adsorption Thermodynamics research. Consequently, using different theoretical assumptions to describe the process complicates comparisons of calculated thermodynamic variable changes. This stagnation in Adsorption Thermodynamics research has limited its practicality. This work aims to restructure the current state of Adsorption Thermodynamics research by establishing a standardized methodology. For specific adsorption processes, a unified thermodynamic system was defined, and the corresponding calculation of thermodynamic variables change was carried out based on reasonable assumptions from various theories. A machine learning (ML)-based text classification model was developed to assist in selecting the most suitable simplified assumption for practical adsorption scenarios. This comprehensive framework was established to deepen the understanding of the adsorption process from a thermodynamic perspective.

Keywords: Adsorption Thermodynamics; Thermodynamic system; Isosteric heat of adsorption; Machine learning; Text classification (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:329:y:2025:i:c:s0360544225024880

DOI: 10.1016/j.energy.2025.136846

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