深度机器学习辅助的MOF@COF光/色度测量双模式智能比度传感平台用于敏感的谷氨检测
Yingfei Hui1, Hao Guo1, Mingyue Wang1
1Key Lab of Eco-Environments Related Polymer Materials of MOE, Key Lab of Bioelectrochemistry and Environmental Analysis of Gansu Province, College of Chemistry and Chemical Engineering, Northwest Normal University, Gansu International Scientific and Technological Cooperation Base of Water-Retention Chemical Functional Materials, Lanzhou, 730070, PR China.
Talanta
|March 23, 2025
概括
本研究引入了一种新的UiO@TBTA材料,用于使用光和色度测量方法检测敏感的谷氨 (GSH). 机器学习提高了对这一重要生物标志物的实时监测的准确性.
科学领域:
- 材料科学 材料科学 材料科学
- 分析化学 分析化学
- 生物医学工程 生物医学工程
背景情况:
- 谷氨 (GSH) 水平在衰老和疾病发病过程中至关重要.
- 在诊断和治疗中需要敏感的分析方法来检测GSH.
- 现有的方法可能面临诸如光材料的聚合引起火 (ACQ) 等挑战.
研究的目的:
- 开发一种新的核心外材料 (UiO@TBTA),用于敏感和多模式的谷氨检测.
- 为GSH建立比度光和色度感测策略.
- 整合机器学习以提高GSH度的准确性和实时监测.
主要方法:
- 通过希夫基反应合成UiO@TBTA复合物,在UiO-66-NH2.2上增加TFPB-TAPACOF.
- 发展以利用内部波器效应和结合为基础的比度光传感.
- 基于可见颜色变化实现色度测量传感.
- 构建人工神经网络模型,用于分析来自光和色度输出的数据.
主要成果:
- UiO@TBTA表现出极好的稳定性,高表面积和减少的ACQ.
- 对GSH的比度光检测在0.1-7μM范围内达到0.0685μM的检测极限 (LOD).
- 颜色测量传感器通过明显的颜色变化检测到1-200μM范围内的GSH.
- 机器学习模型从光和色度数据准确地确定了GSH度.
结论:
- 复合MOF@COF (UiO@TBTA) 为多式联通GSH检测提供了一个新的平台.
- 这种双模式传感策略与人工智能相结合,为实时GSH监控提供了一个有希望的方法.
- 开发的平台在疾病诊断和治疗管理方面具有重大潜力.
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