Citation: Jiaqi Chen, Liang Chen, Xiaocui Wei, Yankai Wang, Yahui Chang, Xinghao Ji, Haoyu Yang, Yue Sun, Yawen Wang, Xiufeng Shi, Xu Wu. Digital Empowerment for Foundational Excellence: A Digitally Enhanced Coordination Titration Experiment of Heating Pack Component Analysis[J]. University Chemistry, ;2026, 41(1): 382-393. doi: 10.12461/PKU.DXHX202506008 shu

Digital Empowerment for Foundational Excellence: A Digitally Enhanced Coordination Titration Experiment of Heating Pack Component Analysis

Jiaqi Chen ^1,2 ,
Liang Chen ¹ ,
Xiaocui Wei ¹ ,
Yankai Wang ¹ ,
Yahui Chang ¹ ,
Xinghao Ji ¹ ,
Haoyu Yang ¹ ,
Yue Sun ^1,2 ,
Yawen Wang ¹ ,
Xiufeng Shi ¹ ,
Xu Wu ^1,,

1.
College of Chemistry and Chemical Engineering, Taiyuan University of Technology, Taiyuan 030024, China;
2.
Experimental Center for Basic Chemistry Education, College of Chemistry and Chemical Engineering, Taiyuan University of Technology, Taiyuan 030024, China

Corresponding author: Xu Wu, wuxu@tyut.edu.cn
Received Date: 3 June 2025
Revised Date: 9 September 2025

Coordination titration serves as a fundamental teaching module in analytical chemistry laboratories, extensively employed for quantitative metal ion analysis. EDTA, the most widely used titrant, exhibits pKa distribution characteristics that facilitate selective titration through pH modulation—a principle thoroughly demonstrated in classical teaching experiments. Commercial self-heating food packets contain precisely formulated calcium-aluminum binary systems (CaO and metallic Al as primary active components). This study designs a stepwise titration protocol enabling simultaneous training in coordination titration techniques under two characteristic pH conditions (alkaline and acidic media) within a single experiment, combining strong pedagogical representativeness with inherent student engagement. Addressing limitations in conventional teaching methods, we developed a digital experimental learning system through this self-heating pack component analysis case study. Guided by student-centered pedagogy, the system seamlessly integrates digital technologies with traditional titration workflows, featuring: real-time feasibility feedback for experimental design, automated titration operation and monitoring, intelligent real-time data acquisition and analysis, and comprehensive student performance evaluation. This integrated approach significantly strengthens students’ experimental competencies while enhancing logical reasoning, analytical judgment, and problem-solving skills, thereby markedly improving experimental teaching efficacy.
- Digital experimental learning system,
- Machine vision,
- Machine learning,
- Coordination titration,
- Heating pack components
1. [1]
2. [2]
3. [3]
4. [4]
5. [5]
6. [6]
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沈阳化工大学材料科学与工程学院沈阳 110142