聚合物珠子的大小透露通过单个实体电化学数据的神经网络分析
Gabriel Gemadzie1, Baosen Zhang1, Aliaksei Boika1
1Department of Chemistry, The University of Akron, Akron, OH 44325, USA. aboika@uakron.edu.
The Analyst
|July 8, 2024
概括
神经网络从电化学数据准确地确定了微塑料的大小. 这种方法克服了微粒子大小分析的挑战,为各种粒子类型提供实时预测.
科学领域:
- 电化学 电化学 电化学
- 纳米技术纳米技术
- 机器学习 机器学习
背景情况:
- 单个实体电化学是微塑料分析的一个有前途的方法.
- 传统的微粒子大小测量方法面临的挑战是磁盘微电极上的电流分布不均.
研究的目的:
- 为了证明神经网络 (NN) 分析在从单个实体的电化学数据中提取尺寸信息的实用性.
- 从实验参数和当前时间数据开发NN模型来预测微粒子半径.
主要方法:
- 开发了完全连接的回归神经网络模型.
- 从单个实体的电化学实验中使用实验参数和当前时间数据训练模型.
- 已验证的模型准确性用于预测单个大小和混合大小微粒的半径.
主要成果:
- NN模型准确地预测了微粒子半径.
- 实现了微粒子大小的近实时预测.
- 对于单个尺寸和混合尺寸的微粒溶液均已证明有效.
结论:
- 神经网络分析对于使用单实体电化学来确定微塑料大小是有效的.
- 这种方法克服了传统方法的局限性,并实现了准确,快速的尺寸.
- 潜在的应用扩展到分析其他生物颗粒,如病毒和细菌.
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