流体分类通过通过水分启用发电的双重功能通过深度学习增强的流体分类
Jiawen Lin1, Hui Dong2,3, Shilong Cui1
1School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou 350108, China.
ACS applied materials & interfaces
|November 7, 2024
概括
这项研究介绍了一种自动供电的传感器,它使用基于水分的发电 (MEG) 和深度学习来快速分类流体. 该系统在15秒内实现了100%的精度,可以在15秒内区分果汁.
科学领域:
- 材料科学与工程 材料科学与工程
- 纳米技术 纳米技术
- 分析化学 分析化学
背景情况:
- 微型传感器对于各种应用中的流体分类至关重要.
- 带湿能发电 (MEG) 设备为自动供电传感提供了潜力.
- 集成先进的材料与微流体学增强传感能力.
研究的目的:
- 通过将MEG与微流体学中的深度学习相结合,开发一种新的智能自主传感方法.
- 为了创建一个双重用途的设备,用于发电和流体检测.
- 为了证明高精度,快速分类不同的液体样本.
主要方法:
- 使用无布,碳纳米管,PVA凝和液态合金制造多层MEG设备.
- 使用复合微流体设计,具有疏水通道和疏水基板用于流量控制.
- 同步测量电压,电流和电阻信号,然后进行深度学习 (WDCNN) 分析.
主要成果:
- 该MEG装置在6个多小时内提供稳定的输出功率.
- 对于不同的流体,产生了不同的电"指纹" (V/C/R信号).
- 一个宽核深卷积神经网络 (WDCNN) 在15秒内实现了100%的水和果汁分类准确度.
结论:
- 整合MEG,微流体学和深度学习为可持续的智能环境感知提供了一个新的范式.
- 这种方法为分析科学和智能仪器的开发提供了创新的前景.
- 开发的系统展示了一种高效和准确的实时流体分析方法.
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