用于水监测的DL辅助的自我体积校准色度PAAHM传感器
Miaorong Lin1, Jihan Qu1, Ting Xiao1
1College of Chemistry and Materials Science, Jinan University, Guangzhou 510632, China. tmjx@jnu.edu.cn.
The Analyst
|February 11, 2026
概括
这项研究介绍了一种新的水凝微球传感器,用于检测水中的,酸盐和铁. 深度学习通过对体积变化进行校准来提高准确性,从而实现精确的环境监测.
科学领域:
- 环境科学 环境科学
- 材料科学 材料科学 材料科学
- 分析化学 分析化学
背景情况:
- 精确的水质监测对于环境保护和公共卫生至关重要.
- 检测氨 (NH4+), (PO43-) 和铁 (Fe2+) 的传统方法可能是复杂且耗时的.
- 开发快速的现场检测方法对于有效的环境管理至关重要.
研究的目的:
- 开发一种新的色度测量传感平台,用于同时检测水中的NH4+,PO43和Fe2+.
- 整合深度学习辅助的自体积校准策略,以提高准确性和效率.
- 创建一个用户友好,低成本,高精度的解决方案,用于现场环境监测.
主要方法:
- 制造具有色度指标的均加载的聚烯酸水凝微球 (PAAHM).
- 利用PAAHM的双色色测量和体积测量响应,用于分析物和体积检测.
- 实施卷积神经网络 (CNN) 用于自体积校准和传感器图像的定量分析.
主要成果:
- 该PAAHM传感器证明了对NH4+,PO43和Fe2+的有效和定量检测.
- 对于度预测,CNN模型实现了0.999的R2相关系数.
- 该系统表现出100%的分类准确性,表明其可靠性很高.
结论:
- 开发的PAAHM传感器具有深度学习校准,为现场水质监测提供了一种方便,低成本和准确的方法.
- 这种方法简化了传感器的制造,并使快速的大规模生产成为可能.
- 这项技术在环境监督和管理方面具有广泛应用的巨大潜力.
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