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基于机器学习的Cd (II) 和Cu (II) 在Co2P/CoP异构上的同时量化:通过激活的Co-P电子桥来增强电化学信号
Zhi-Wei Gao1, Yan Yu2, Shi-Hua Chen2
1Key Laboratory of Environmental Optics and Technology, And Environmental Materials and Pollution Control Laboratory, Institute of Solid State Physics, HFIPS, Chinese Academy of Sciences, Hefei 230031, China; Department of Materials Science and Engineering, University of Science and Technology of China, Hefei 230026, China; Institute of Environment, Hefei Comprehensive National Science Center, Hefei 230088, China.
机器学习使用电化学方法同时准确量化像 (Cd(II)) 和铜 (Cu(II)) 这样的重金属. 这种方法克服了信号干扰,从而实现了精确的环境监测.
科学领域:
- 电化学 电化学 电化学
- 材料科学 材料科学 材料科学
- 机器学习 机器学习
- 环境科学 环境科学
背景情况:
- 使用电化学方法同时量化多个重金属离子是具有挑战性的,因为信号干扰和复杂的数据分析.
- 现有的校准曲线方法与来自干扰信号的高通量数据作斗争,限制了环境监测的准确性.
研究的目的:
- 引入机器学习技术,共同检测 (Cd) 和铜 (Cu) 离子.
- 在Co2P/CoP异构结构上探索Cd (II) 和Cu (II) 之间的电化学干扰机制.
- 开发一种可靠的重金属同时检测方法,克服传统电化学分析的局限性.
主要方法:
- 使用高活性Co2P/CoP异构结构作为电化学传感平台.
- 采用随机森林 (RF) 模型来确定电化学反应电流中的关键特征变量.
- 应用了一个卷积神经网络 (CNN) 模型来确定电化学信号和离子度之间的关系.
主要成果:
- 实现了同时检测Cd (II) 和Cu (II) 的卓越可靠性,两种离子的R2值为0.996.
- 具有较低根平均平方误差 (RMSE) 值的高预测准确性:Cd(II) 的0.0177μM和Cu(II) 的0.0206μM.
- 揭示了Cu(II) 首选与P位相结合,而不是Cd(II),影响电化学信号生成和干扰机制.
结论:
- 机器学习 (RF和CNN) 的整合为同时电化学检测多个重金属离子提供了强大的解决方案.
- 了解优先结合和干扰机制可以提高电化学传感的准确性和可靠性.
- 这种方法显著加快了电化学方法在重金属污染环境监测中的实际应用.
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