Ultra-sensitive detection of PFASs using surface enhanced Raman scattering and machine learning: a promising approach for environmental analysis†

IF 3.5 Q2 CHEMISTRY, ANALYTICAL
Joshua C. Rothstein, Jiaheng Cui, Yanjun Yang, Xianyan Chen and Yiping Zhao
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Abstract

The contamination of per- and polyfluoroalkyl substances (PFAS) in drinking water presents a significant concern and requires a simple, portable detection method. This study aims to demonstrate the effectiveness of Raman and surface-enhanced Raman scattering (SERS) spectroscopies for identifying and quantifying various PFASs in water. Experimental Raman spectra of different PFASs reveal unique characteristic peaks that enable their classification. While direct SERS measurements from silver nanorod (AgNR) substrates may not exhibit distinct PFAS characteristic peaks, the presence of PFAS on SERS substrates induces noticeable spectral changes. By integration with machine learning (ML) techniques, these SERS spectra can be used to successfully differentiate and quantify PFOA in water, achieving a limit of detection (LOD) of 1 ppt. Modifying the AgNR substrates with cysteine and 6-mercapto-1-hexanol enhances the differentiation and quantification capabilities of SERS-ML. Despite alkanethiol molecules affecting spectral features, PFAS and PFOS concentrations produce observable spectral variations. A support vector machine model achieves 93% accuracy in differentiating PFOA, PFOS, and references, independent of concentration. A support vector regression model further establishes LODs of 1 ppt for PFOA and 4.28 ppt for PFOS. By removing spectra with concentrations lower than LODs, the classification accuracy is improved to 95%.

Abstract Image

利用表面增强拉曼散射和机器学习对 PFAS 进行超灵敏检测:一种前景广阔的环境分析方法
饮用水中的全氟烷基和多氟烷基物质(PFAS)污染是一个重大问题,需要一种简单、便携的检测方法。本研究旨在证明拉曼光谱和表面增强拉曼散射(SERS)光谱在识别和量化水中各种 PFAS 方面的有效性。不同全氟辛烷磺酸的实验拉曼光谱显示出独特的特征峰,可对其进行分类。虽然银纳米棒(AgNR)基底的直接 SERS 测量可能不会显示出明显的 PFAS 特征峰,但 SERS 基底上 PFAS 的存在会引起明显的光谱变化。通过与机器学习(ML)技术相结合,这些 SERS 光谱可用于成功区分和量化水中的全氟辛烷磺酸,检测限(LOD)达到 1 ppt。用半胱氨酸和 6-巯基-1-己醇修饰 AgNR 底物增强了 SERS-ML 的分辨和定量能力。尽管烷硫醇分子会影响光谱特征,但全氟辛烷磺酸和全氟辛烷磺酸的浓度会产生可观察到的光谱变化。支持向量机模型在区分全氟辛烷磺酸、全氟辛烷磺酸和参照物方面达到了 93% 的准确率,与浓度无关。支持向量回归模型进一步确定了 PFOA 的检测限为 1 ppt,PFOS 的检测限为 4.28 ppt。通过去除浓度低于 LOD 的光谱,分类准确率提高到 95%。
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CiteScore
2.30
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0.00%
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