大学化学

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超分子化学前沿融入基础实验教学：罗丹明B-环糊精键合作用的热力学探究

周维磊1, 韩赫1, 陈湧2, 许秀芳2   

  1. 1 内蒙古民族大学化学与材料学院, 内蒙古 通辽 028000;
    2 南开大学化学学院, 天津 300071
  • 收稿日期:2025-12-26 修回日期:2026-03-04
  • 通讯作者: 周维磊, 许秀芳 E-mail:zhouweilei_2011@163.com;xxfang@nankai.edu.cn Weilei Zhou, Xiufang Xu
  • 基金资助:
    国家自然科学基金地区项目(22361036)； 国家自然科学基金委面上基金项目(22571173)； 内蒙古自治区优秀青年基金项目(2025YQ050)；内蒙古民族大学教育教学研究课题(YB2025003)

Integrating supramolecular chemistry frontiers into fundamental experimental education: thermodynamic investigation of the bonding interaction between rhodamine B and cyclodextrin

Weilei Zhou1, He Han1, Yong Chen2, Xiufang Xu2   

  1. 1 College of Chemistry and Material Science, Inner Mongolia Minzu University, Tongliao 028000, Inner Mongolia Autonomous Region, China;
    2 College of Chemistry, Nankai University, Tianjin 300071, China
  • Received:2025-12-26 Revised:2026-03-04
  • Contact: Weilei Zhou, Xiufang Xu E-mail:zhouweilei_2011@163.com;xxfang@nankai.edu.cn

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摘要： 针对当前大学化学实验中科学前沿内容缺失与学生创新能力培养不足的问题，设计了一个将超分子化学前沿与物理化学基础实验深度融合的综合性教学案例。实验以罗丹明B与α-、β-、γ-环糊精的包结作用为研究模型，利用紫外-可见分光光度计监测主客体相互作用。区别于传统验证性实验，本研究引入了人工智能辅助的数据处理新方法：指导学生基于1:1结合模型，通过AI交互式学习并自主构建MATLAB非线性拟合程序，从吸光度-浓度数据中精确计算结合常数(K)与吉布斯自由能变(ΔG)等热力学参数。该设计使学生完整经历了“提出假设→实验验证→程序设计→数据分析→结果探讨→报告反思”的科研全流程。实践表明，该方案不仅将经典的仪器分析技术与现代计算科学有效结合，更通过解决真实科研问题，系统性地培养了学生的计算思维、数据分析与自主探究能力，更为推进基于前沿科研成果的实验教学改革提供了具体可行的实践路径。

关键词: 超分子化学, 实验教学改革, 键合常数, 创新能力培养

Abstract: To address the current gaps in incorporating cutting-edge scientific content and fostering students' innovative capacities in university chemistry experiments, this study developed a comprehensive teaching module that seamlessly integrates supramolecular chemistry advancements with fundamental physical chemistry experiments. Using the host-guest complexation of rhodamine B with α-, β-, and γ- cyclodextrins as a model system, we employed UV-Vis spectrophotometry to monitor molecular interactions. Departing from conventional verification experiments, this work introduces an innovative AIassisted data processing approach: students were guided to develop MATLAB-based nonlinear fitting programs through interactive AI learning, employing a 1:1 binding model to accurately determine thermodynamic parameters including bonding constants (K) and Gibbs free energy changes (ΔG) from absorbance-concentration data. This pedagogical design facilitated students' complete engagement in the scientific research cycle: hypothesis generation → experimental validation → program development → data analysis → results interpretation → reflective reporting. The implementation demonstrated that this approach not only effectively combines classical instrumental analysis with modern computational methods, but also systematically enhances students’ computational thinking, data analysis skills, and independent research capabilities through authentic scientific problem-solving. Furthermore, it establishes a practical framework for advancing experimental teaching reform through the incorporation of frontier research achievements.

Key words: Supramolecular chemistry, Reform of laboratory instruction, Bonding constant, Innovation capability cultivation