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OA

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.