大学化学

上一篇    

蔗糖水解实验旋光度测量软件的优化与应用

李林桐, 杨力行, 刘景南, 赵浩, 杨玲, 吴忠云, 徐金荣   

  1. 北京大学化学与分子工程学院, 化学基础国家级实验教学示范中心(北京大学), 北京 100871
  • 收稿日期:2026-04-16 录用日期:2026-05-22
  • 通讯作者: 徐金荣 E-mail:xujinrong@pku.edu.cn Jinrong Xu
  • 基金资助:
    “拔尖计划2.0课题：人工智能赋能的化学实验平台建设(20251006)

Software optimization and application for an intelligent optical rotation measurement system in sucrose hydrolysis experiments

Lintong Li, Lixing Yang, Jingnan Liu, Hao Zhao, Ling Yang, Zhongyun Wu, Jinrong Xu   

  1. National Demonstration Center for Experimental Chemistry Education (Peking University), College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China
  • Received:2026-04-16 Accepted:2026-05-22
  • Contact: Jinrong Xu E-mail:xujinrong@pku.edu.cn

PDF

32

赞

摘要： 蔗糖水解反应是物理化学实验教学中的经典动力学实验。基于已开发的旋光仪视场智能识别与旋光度自动测量实验系统，本研究进行了一系列软件优化：开发了训练集录制、模型管理、拟合分析等内置功能模块，提升了操作效率；改进步进电机控制策略，灵活调整步进模式，提高了动态测量的响应速度和灵活性；优化图像预处理环节，提升了训练数据和待测数据质量，从而增强了旋光度测量的泛化能力。此外，用户界面采用标签页布局进行了重新设计，提高了各个功能组件的模块化程度和操作逻辑的清晰度。实验验证表明，本优化有效提升了实验效率和数据质量，减轻了学生的操作负担，为蔗糖水解实验的数字化教学提供了更适用的工具支持。

关键词: 蔗糖水解, 旋光仪, 机器学习, 自动化测量, 软件优化

Abstract: The sucrose hydrolysis reaction is a classic kinetic experiment in physical chemistry education. Building on a previously developed experimental system that integrates intelligent recognition of the polarimeter’s field of view and automatic optical rotation measurement, this study introduces a series of software optimizations. Built-in functional modules for training set recording, model management, and fitting analysis were developed to enhance operational efficiency. The stepper motor control strategy was refined to allow flexible adjustment of the stepping mode, thereby improving the response speed and flexibility of dynamic measurements. The image preprocessing stage was optimized to enhance the quality of both training and measurement data, which in turn strengthened the generalization capability of the optical rotation measurement. Additionally, the user interface was redesigned using a tab-based layout, improving the modularity of functional components and the clarity of the operational logic. Experimental validation demonstrated that these optimizations effectively improved experimental efficiency and data quality, reduced the operational burden on students, and provided a more practical tool to support intelligent and digital teaching in the context of sucrose hydrolysis experiments.

Key words: Sucrose hydrolysis, Polarimeter, Machine learning, Automated measurement, Software optimization