引用本文:
王月娇, 毛全兴, 崔俊硕, 冯小庚, 常晓红, 娄振宁, 熊英. 颜色识别式人工智能融入混合碱滴定实验的应用[J]. 大学化学,
2026, 41(1): 107-113.
doi:
10.12461/PKU.DXHX202506020
Citation: Yuejiao Wang, Quanxing Mao, Junshuo Cui, Xiaogeng Feng, Xiaohong Chang, Zhenning Lou, Ying Xiong. 颜色识别式人工智能融入混合碱滴定实验的应用[J]. University Chemistry, 2026, 41(1): 107-113. doi: 10.12461/PKU.DXHX202506020
Citation: Yuejiao Wang, Quanxing Mao, Junshuo Cui, Xiaogeng Feng, Xiaohong Chang, Zhenning Lou, Ying Xiong. 颜色识别式人工智能融入混合碱滴定实验的应用[J]. University Chemistry, 2026, 41(1): 107-113. doi: 10.12461/PKU.DXHX202506020
颜色识别式人工智能融入混合碱滴定实验的应用
English
颜色识别式人工智能融入混合碱滴定实验的应用
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.
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Key words:
- Mixed Alkali
- / Titration
- / Python
- / Artificial Intelligence
- / Color-discrimination
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