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在废水中检测抗生素的痕迹,使用可调节的核心纳米颗粒SERS基底与机器学习算法相结合

Muhammad Usman¹, Wajid Ali², Saleh S Alarfaji³

¹Institute of Materials Science, Kaunas University of Technology, K. Baršausko St. 59, LT-51423 Kaunas, Lithuania.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy

|January 10, 2025

概括

表面增强拉曼散射 (SERS) 提供了对废水中抗生素污染的敏感检测. 一种新的纳米粒子方法与机器学习相结合,准确量化了抗生素残留物,在支持矢量机模型中达到99%的准确性.

科学领域:

环境科学环境科学
分析化学分析化学
纳米技术纳米技术

背景情况:

水生生态系统中的抗生素污染带来了重大环境和健康风险,包括抗生素耐药细菌的发展.
污水处理需要有效的方法来检测和量化残留的抗生素.

研究的目的:

开发一种新,快速和高度敏感的方法,用于在废水中定量检测抗生素.
利用表面增强的拉曼散射 (SERS) 与抗生素分析的计算方法相结合.

主要方法:

核心外纳米粒子 (GNRs@1,4-BDT@Ag) 为SERS应用进行了合成.
获得了不同度的西普罗夫洛克萨辛和勒沃弗洛克萨辛的SERS光谱.
机器学习算法,包括主要组件分析 (PCA),正交部分最小平方差分分析 (OPLS-DA) 和支持矢量机器 (SVM),用于光谱分析和分类.

主要成果:

开发的SERS方法成功地识别和量化了废水溶液中的抗生素残留物.
机器学习模型在预测抗生素状态方面表现出很高的准确性,SVM的准确性高达99%.
PCA和OPLS-DA对于聚类SERS光谱数据集是有效的.

结论:

GNRs@1,4-BDT@Ag纳米颗粒与SERS和机器学习相结合,为废水中的抗生素检测提供了一个强大的工具.
这种方法为环境监测提供了一个简单,快速和准确的方法.
该技术显示出在分析各种领域污染物的更广泛应用的潜力.

关键词:

抗生素抗生素是一种抗生素.检测灵敏度检测灵敏度检测灵敏度 GNRs@Ag核心外纳米粒子机器学习算法机器学习算法表面增强的拉曼光谱学

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