超越莱维奇近似的旋转磁盘电极:基于物理的神经网络揭示和量化边缘效应
Haotian Chen1, Enno Kätelhön2, Richard G Compton1
1Department of Chemistry, Physical and Theoretical Chemistry Laboratory, Oxford University, South Parks Road, Oxford OX1 3QZ, Great Britain.
Analytical chemistry
|August 17, 2023
概括
基于物理学的神经网络 (PINNs) 在旋转磁盘电极 (RDE) 中准确地模拟质量传输,超过了各种施密特数的Levich方程限制. PINNs揭示了RDE边缘效应,为传统电分析方法提供了强大的替代方案.
科学领域:
- 电化学 电化学 电化学
- 计算科学 计算科学
- 机器学习 机器学习
背景情况:
- 旋转盘电极 (RDE) 是电分析中的一个基本工具,但它的理论模型,如利维奇近似,有局限性.
- 利维奇近似仅对高的施密特数 (Sc>1000) 准确,这可能导致迅速扩散的物种存在重大错误.
- 传统的RDE分析方法包括分析方程和数值模拟,这些可能是复杂的或范围有限的.
研究的目的:
- 将物理信息的神经网络 (PINNs) 应用于对RDE进行质量运输的表征.
- 检验PINN方法与既定方法对比,并探索其超出Levich近似的局限性的准确性.
- 使用开发的PINN模型,研究RDE行为的新方面,例如边缘效应.
主要方法:
- 在RDE条件下,使用物理信息神经网络 (PINNs) 来解决扩散方程.
- 使用1D模拟量化验证PINN模型,并将结果与分析方程和有限差异方法进行比较.
- 这项研究将PINN方法扩展到2D圆柱形几何,并结合了辐射扩散效应.
主要成果:
- 对于大规模运输的表征,PINNs实现了分析级准确性 (<0.1%的误差),即使在较低的施密特数下,利维奇近似也失败了.
- 该研究证实,在特定条件下,Levich方程可以在Sc = 1000时引入高达3%的误差.
- 新的是,PINNs揭示并量化了RDE边缘效应,证明了由于辐射扩散导致磁盘极端附近电流增加.
结论:
- 基于物理学的神经网络为RDE大众运输分析提供了比传统方法更准确和更通用的工具.
- PINNs成功地将RDE理论扩展到利维奇近似之外,使得研究诸如边缘效应等复杂现象成为可能.
- 这些发现表明PINNs是电分析中传统的分析和仿真方法的强大和潜在的更简单的替代品.
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