大学化学 >> 2026, Vol. 41 >> Issue (1): 107-113.doi: 10.12461/PKU.DXHX202506020

所属专题： 化学实验数字化设计竞赛获奖作品

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颜色识别式人工智能融入混合碱滴定实验的应用

王月娇, 毛全兴, 崔俊硕, 冯小庚, 常晓红, 娄振宁, 熊英   

  1. 辽宁大学化学院, 沈阳 110036
  • 收稿日期:2025-06-03 录用日期:2025-08-27 发布日期:2025-12-30
  • 通讯作者: 熊英 E-mail:xiongying@lnu.edu.cn Ying Xiong
  • 基金资助:
    辽宁省科学技术计划项目(2025-BS-0293)

Application of Color-Discriminable Artificial Intelligence into Mixed Alkali Titration Experiments

Yuejiao Wang, Quanxing Mao, Junshuo Cui, Xiaogeng Feng, Xiaohong Chang, Zhenning Lou, Ying Xiong   

  1. College of Chemistry, Liaoning University, Shenyang 110036, China
  • Received:2025-06-03 Accepted:2025-08-27 Published:2025-12-30
  • Contact: Ying Xiong E-mail:xiongying@lnu.edu.cn

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摘要： 在混合碱滴定分析实验中，存在第一化学计量点终点判读主观性强、三次平行测定误差大的缺点。本文利用颜色识别式人工智能(Color-discriminable Artificial Intelligence，CDAI)进行终点判断，通过硬件精确控制与智能算法相结合，建立客观、可量化的酚酞和甲基橙的动态颜色判别模型，有效解决了终点判读及重现性差问题。本文探索并实践了一种新型智能化的滴定实验方法，为化学实验教学的数字化转型提供了可推广的实践方案。

关键词: 混合碱, 滴定, Python, 人工智能, 颜色识别

Abstract: Conventional mixed alkali titration analysis suffers from subjective endpoint determination at the first equivalence point and significant variability in triplicate measurements. This study employs Color-Discriminable Artificial Intelligence (CDAI) for endpoint detection, combining precise hardware control with intelligent algorithms to establish an objective, quantifiable dynamic color discrimination model for phenolphthalein and methyl orange indicators. The developed system effectively resolves endpoint determination challenges and improves measurement reproducibility. This work demonstrates an innovative intelligent titration methodology, offering a scalable solution for the digital transformation of chemical laboratory education.

Key words: Mixed Alkali, Titration, Python, Artificial Intelligence, Color-discrimination