Machine Learning Based Research-Type Chemistry Experiment:Three-Channel Spectroscopic Technique for Simultaneous Detection of Co2+and Cu2+Ions
LI Xin-Yue1, LIANG Rui-Ying2, PEI Yi-Nuo1, JIN Shi-Yu1, LI Xin-Ran3, LIANG Jian-Gong1, LIU Ling-Zhi1**
1. College of Chemistry, Huazhong Agricultural University, Wuhan 430070, China; 2. School of Software Engineering, Huazhong University of Science and Technology, Wuhan 430074, China; 3. College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
Abstract Artificial intelligence, especially machine learning, is opening up new possibilities for traditional chemistry experiments. Developing machine learning based research-type experiments is of great significance for cultivating students’ digital literacy and innovative capabilities. Herein, we report a digitized upgrade of a previous experiment on single metal ion detection. The modified experiment encompasses the simultaneous detection of Co2+ and Cu2+ ions via three-channel spectroscopic technique and the application of machine learning. Both random forest and multi-layer perceptron prediction models were established, which achieved efficient identification and accurate quantification of single and binary metal ions. Satisfactory results were achieved in real lake water analysis. The established research-type experiment not only provides a new approach for multicomponents analysis in complex samples, but also serves as a robust example for the digital-intelligent reform of experimental teaching. The experiment has been successfully implemented in teaching practice with positive educational outcomes.
LI Xin-Yue, LIANG Rui-Ying, PEI Yi-Nuo, JIN Shi-Yu, LI Xin-Ran, LIANG Jian-Gong, LIU Ling-Zhi. Machine Learning Based Research-Type Chemistry Experiment:Three-Channel Spectroscopic Technique for Simultaneous Detection of Co2+and Cu2+Ions[J]. Chinese Journal of Chemical Education, 2026, 47(4): 106-116.
