数字化助力多孔材料的染料吸附实验

doi:10.12461/PKU.DXHX202504072

摘要/Abstract

摘要： 吸附是一个常见且重要的物理化学过程，吸附实验也越来越多地出现在实验教学当中。然而，传统的教学模式使学生往往只能被动接受实验方案，缺乏对吸附剂选择和微观吸附机理等方面的深入理解。为此，我们提出一种基于人工智能(AI)的数字化实验设计，旨在通过生成式AI大模型的智能分析和分子动力学(MD)模拟，使学生既可以进行实验前的性能预测，又可以进行实验前后的人机对话预习与总结。学生首先通过互动式对话使用ChatGPT智能助手进行文献收集、整理、吸附剂筛选和个性化预习。随后利用MD模拟预测吸附剂性能，探索吸附机制。再对模拟预测结果进行实验验证，测试吸附动力学和热力学。最后针对实验中的问题和思考与ChatGPT智能助手互动。本实验设计实现了从人机对话的个性化预习、吸附过程的数值模拟，到实验验证，再到人机对话总结的全流程数字化教学，使学生从被动的操作执行者成长为主动的学习参与者和研究探索者，为传统实验教学的现代化和智能化提供了新思路。

关键词: 吸附实验, 数字化教学, 人工智能, 分子动力学模拟, ChatGPT

Abstract: Adsorption represents a fundamental physicochemical process, and adsorption experiments are being increasingly integrated into undergraduate laboratory curricula. However, conventional teaching approaches often confine students to passively executing predetermined experimental procedures, limiting their comprehension of critical aspects such as adsorbent selection and microscopic adsorption mechanisms. To address these limitations, we propose an AI-enhanced digital experimental framework that combines generative AI models with molecular dynamics (MD) simulations. This innovative approach enables students to predict adsorption performance pre-experiment and engage in interactive pre- and post-experimental discussions through human-computer dialogue. The experimental workflow begins with students utilizing ChatGPT as an intelligent assistant for literature review, data organization, adsorbent screening, and personalized preparation via interactive dialogue. Subsequently, MD simulations are employed to predict adsorbent performance and investigate adsorption mechanisms. Experimental validation then follows to examine adsorption kinetics and thermodynamics in comparison with simulation predictions. Finally, students can engage in reflective discussions with ChatGPT regarding experimental observations and conceptual questions. This comprehensive digital teaching methodology encompasses interactive preparation, numerical simulation of adsorption processes, experimental verification, and AI-assisted reflection, effectively transforming students from passive executors into active learners and researchers. The approach presents a novel paradigm for modernizing and digitizing traditional experimental pedagogy.

Key words: Adsorption experiment, Digital teaching, Artificial intelligence (AI), Molecular dynamics (MD) simulations, ChatGPT

陈宁远, 曾庆宜, 况志强, 田佩聪, 颜大智, 靳伟良, 孔德明. 数字化助力多孔材料的染料吸附实验[J]. 大学化学, 2026, 41(1): 321-331.

Ningyuan Chen, Qingyi Zeng, Zhiqiang Kuang, Peicong Tian, Dazhi Yan, Weiliang Jin, Deming Kong. Digital-Assisted Experimental Study on Dye Adsorption by Porous Materials[J]. University Chemistry, 2026, 41(1): 321-331.

链接本文: https://www.dxhx.pku.edu.cn/CN/10.12461/PKU.DXHX202504072

https://www.dxhx.pku.edu.cn/CN/Y2026/V41/I1/321

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