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基于 triboelectric纳米发电机和机器学习的水污染物的实时检测

Zhijie Zhang¹, Wei Long², Peiyin Liu¹

¹Faculty of Mechanical and Electrical Engineering, Kunming University of Science and Technology, Kunming, 650500, P.R. China.

Environmental monitoring and assessment

|March 10, 2026

概括

一种新型的自动供电水传感器使用 triboelectricity 来检测重金属和微塑料等污染物. 该系统为智能水质监测提供了一种可靠的方法.

科学领域:

环境科学环境科学
材料科学材料科学材料科学
传感器技术传感器技术

背景情况:

水质对人类活动和生态系统健康至关重要.
现有的水源监测方法可能是复杂而昂贵的.
需要有效的实时水质评估工具.

研究的目的:

开发一种自动供电的水质传感系统.
为了利用接触电气化和 triboelectric 效应用于污染物检测.
整合机器学习以准确预测水质.

主要方法:

使用导电海绵制造水质传感系统.
从污染物-液体-固体相互作用中产生 triboelectric 信号的生成.
使用电表和光梯度增强机 (Light GBM) 模型进行数据采集和分析.

主要成果:

有效识别重金属离子 (Zn2+,Ba2+,Al3+),聚烯微塑料和生 (Fe2O3).
污染物检测的平均分类准确率为86.67%.
在不同温度,pH值和度下稳定而准确的检测 (高达100%).

结论:

综合系统为智能水质监测提供了一个有前途的战略.
自动驱动的 triboelectric 传感与机器学习相结合,提供了一种新的方法.
该系统在现实世界水样中证明了可靠性和准确性.

关键词:

分类的准确性分类的准确性轻 GBM 模型模型液体固体接触电气化的电气化在开放电路中,电压是开放的.水污染物的预测预测

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