大学化学2026，Vol.41Issue(1)：76-84,9.DOI:10.12461/PKU.DXHX202506023
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
Machine Learning-Optimized Microcolumn Ion Exchange Chromatography for Trace Arsenic Determination
摘要
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.关键词
机器学习/微色谱柱/痕量砷/机器视觉Key words
Machine learning/Microcolumn chromatography/Trace arsenic/Machine vision分类
社会科学引用本文复制引用
常灵宇,刘淑娟,郎艳芳,朱玉妍,王婕,郭莹,王蝶,丁鹏,周跃明,龚治湘..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[J].大学化学,2026,41(1):76-84,9.基金项目
江西省高等学校教学改革研究省级课题(JXJG-23-6-34) （JXJG-23-6-34）
东华理工大学实验技术开发项目(DHSY-202313,DHSY-202511) （DHSY-202313,DHSY-202511）
东华理工大学教学改革研究课题(DHJG-23-33) （DHJG-23-33）
