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.
