机器学习优化的微色谱柱离子交换色谱法测定痕量砷实验

doi:10.12461/PKU.DXHX202506023

University Chemistry ›› 2026, Vol. 41 ›› Issue (1): 76-84.doi: 10.12461/PKU.DXHX202506023

Special Issue:

• Special Subject • Previous Articles Next Articles

Machine Learning-Optimized Microcolumn Ion Exchange Chromatography for Trace Arsenic Determination

Lingyu Chang¹, Yanfang Lang¹, Yuyan Zhu¹, Jie Wang¹, Ying Guo¹, Die Wang², Peng Ding³, Yueming Zhou¹, Zhixiang Gong¹, Shujuan Liu¹

1 School of Chemistry and Materials Science, East China University of Technology, Nanchang 330013, China;
2 School of Modern Agriculture, Xiangxi Vocational and Technical College for Nationalities, Xiangxi 416099, Hunan Province, China;
3 School of Information Engineering, East China University of Technology, Nanchang 330013, China

Received:2025-06-03 Accepted:2025-10-20 Published:2025-12-30
Contact: Yueming Zhou E-mail:ymzhou@ecut.edu.cn

Abstract

Abstract: Microcolumn ion exchange chromatography demands rigorous standardization and operational proficiency from students. This study investigates a machine vision-based approach to capture time-series behavioral data during on-column operations, establishing a comprehensive experimental variable database. By integrating experimentally acquired data with machine learning algorithms, we elucidate underlying patterns and employ digital twin technology for real-time process monitoring. This methodology enables identification of error sources and critical factors influencing result stability. Implemented as an instructional experiment, this approach cultivates students’ ability to apply digital technologies to address analytical chemistry challenges.

Key words: Machine learning, Microcolumn chromatography, Trace arsenic, Machine vision

Lingyu Chang, Yanfang Lang, Yuyan Zhu, Jie Wang, Ying Guo, Die Wang, Peng Ding, Yueming Zhou, Zhixiang Gong, Shujuan Liu. Machine Learning-Optimized Microcolumn Ion Exchange Chromatography for Trace Arsenic Determination[J].University Chemistry, 2026, 41(1): 76-84.

[1]	Kai Ye, Lizhong Zhang, Mingyu Zhang, Qinxiong Wu, Kui Wang, Qi Wang. Digital Experiment for the Determination of Liquid Saturated Vapor Pressure [J]. University Chemistry, 2026, 41(1): 227-243.
[2]	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.
[3]	Heng Zhang, Ying Ma, Shiling Yuan. Machine Learning-based Prediction of Antifouling Performance in Polymer Materials: An Integrated Molecular Simulation Experiment [J]. University Chemistry, 2026, 41(1): 346-353.
[4]	Jingjie Rao, Wenwen Cai, Jiahui Zhao, Xu Yang, Ziyan Yan, Tianjin Zhang, Hang Zhang. Digital Exploration of Analytical Chemistry Experiments in the Context of Machine Learning and Big Data: A Case Study on Water Hardness Measurement [J]. University Chemistry, 2026, 41(1): 276-288.
[5]	Zuoyong Li, Haoxiang Tu, Mingwei Ding, Meijun Liu, Ting Yang. Innovative Teaching Reform Study on the Synthesis of Silver Nanoparticles Based on Machine Learning and Microfluidic Technology [J]. University Chemistry, 2026, 41(1): 64-75.
[6]	Yueming Zhou, Xin Qiu, Xin Zhou, Xiaotian Wan, Mofan Zhang, Feng Li, Xinxin Shao, Peng Ding, Xizhen Liang. Intelligent Visualization of Potassium Dichromate Reflux Method for Determination of Chemical Oxygen Demand [J]. University Chemistry, 2026, 41(1): 85-94.
[7]	Zhican Lu, Junyu Li, Zijun Huang, Ziyi Zeng, Chi Huang, Chuqing Gong, Yalan Zhong. Digital Experimental Design of Decomposition of Ammonium Perchlorate Catalyzed by MOFs [J]. University Chemistry, 2026, 41(1): 133-143.
[8]	Songmei Ma, Ying Zhang, Gang Liu, Wenlong Xu. Comprehensive Experiment Teaching Exploration and Practice in Polymeric Materials Integrating Research-Driven Learning, Creativity-Enhanced Competency, and Science-Education Synergy: A Case Study of Machine Learning-Assisted Intelligent Handwriting Recognition System [J]. University Chemistry, 2026, 41(1): 289-297.
[9]	Weigang Zhu, Jianfeng Wang, Qiang Qi, Jing Li, Zhicheng Zhang, Xi Yu. Curriculum Development for Cheminformatics and AI-Driven Chemistry Theory toward an Intelligent Era [J]. University Chemistry, 2025, 40(9): 34-42.
[10]	Jia Zhou. Design and Practice of a Comprehensive Computational Chemistry Experiment Based on High-Throughput Computation and Machine Learning [J]. University Chemistry, 2025, 40(9): 69-75.
[11]	Jianqiang Zheng, Yongbin Huang, Wencan Ming, Yingju Liu. Intelligent Reaction Optimization: Synthesis of Acetylsalicylic Acid Driven by Deep Learning and Optimization Algorithms [J]. University Chemistry, 2025, 40(9): 87-98.
[12]	Jia Zhou, Huaying Zhong. Experimental Design of Computational Materials Science Combined with Machine Learning [J]. University Chemistry, 2025, 40(3): 171-177.
[13]	Siyuan Zhang, Zhicheng Zhang, Rongjin Li. Applications of Machine Learning in Chemistry [J]. University Chemistry, 2025, 40(1): 206-218.
[14]	Jia Zhou. Constructing Potential Energy Surface of Water Molecule by Quantum Chemistry and Machine Learning: Introduction to a Comprehensive Computational Chemistry Experiment [J]. University Chemistry, 2024, 39(3): 351-358.
[15]	Haoran Qi, Mingqing Lei, Shuchang Guo, Ziqi Jiang, Yikai Zhang, Ziyan Shen. Design and Implementation of a Solution Concentration Monitoring Device Based on OpenCV [J]. University Chemistry, 2024, 39(12): 378-384.

Machine Learning-Optimized Microcolumn Ion Exchange Chromatography for Trace Arsenic Determination

PDF

Like

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Metrics

Recommended 0