Increasing accumulation of nanoplastics across ecosystems poses a significant threat to both terrestrial and aquatic life. Surface-enhanced Raman scattering (SERS) is an emerging technique used for nanoplastics detection. However, the identification and classification of nanoplastics using SERS faces challenges regarding sensitivity and accuracy as nanoplastics are sparsely dispersed in the environment. Metal–phenolic networks (MPNs) have the potential to rapidly concentrate and separate various types and sizes of nanoplastics. SERS combined with machine learning may improve prediction accuracy. Herein, we report the integration of MPNs-mediated separation with machine learning-aided SERS methods for the accurate classification and high-precision quantification of nanoplastics, which is tailored to include the complete region of characteristic peaks across diverse nanoplastics in contrast to the traditional manual analysis of SERS spectra on a singular characteristic peak. Our customized machine learning system (e.g., outlier detection, classification, quantification) allows for the identification of detectable nanoplastics (accuracy 81.84%), accurate classification (accuracy > 97%), and sensitive quantification of various types of nanoplastics (polystyrene (PS), poly(methyl methacrylate) (PMMA), polyethylene (PE), and poly(lactic acid) (PLA)) down to ultralow concentrations (0.1 ppm) as well as accurate classification (accuracy > 92%) of nanoplastic mixtures at a subppm level. The effectiveness of this approach is substantiated by its ability to discern between different nanoplastic mixtures and detect nanoplastic samples in natural water systems.

中文翻译：

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集成金属-酚网络介导的分离和机器学习辅助的表面增强拉曼光谱，以实现准确的纳米塑料定量和分类


生态系统中纳米塑料的不断积累对陆地和水生生物构成了重大威胁。表面增强拉曼散射（SERS）是一种用于纳米塑料检测的新兴技术。然而，由于纳米塑料在环境中稀疏分散，使用SERS对纳米塑料进行识别和分类面临着灵敏度和准确性方面的挑战。金属酚网络（MPN）具有快速浓缩和分离各种类型和尺寸的纳米塑料的潜力。 SERS与机器学习相结合可以提高预测准确性。在此，我们报告了 MPN 介导的分离与机器学习辅助的 SERS 方法的集成，用于纳米塑料的准确分类和高精度定量，与传统的手动方法相比，该方法经过定制，包括不同纳米塑料的特征峰的完整区域奇异特征峰的SERS光谱分析。我们定制的机器学习系统（例如异常值检测、分类、定量）可以识别可检测的纳米塑料（准确度 81.84%）、准确分类（准确度 > 97%）以及对各类纳米塑料（聚苯乙烯（PS） ）、聚（甲基丙烯酸甲酯）（PMMA）、聚乙烯（PE）和聚（乳酸）（PLA））低至超低浓度（0.1 ppm），并对纳米塑料混合物进行准确分类（准确度 > 92%）亚ppm水平。这种方法的有效性得到了其区分不同纳米塑料混合物和检测天然水系统中纳米塑料样品的能力的证实。