水中微塑料的射频检测与分类

中文摘要 AI 辅助

微米级和纳米级塑料颗粒（MPs/NPs）是普遍存在的环境污染物，其日益增加的丰度和潜在的健康影响使得对快速、无标记检测方法的需求变得迫切。随着颗粒尺寸减小到低微米范围，传统的光学和光谱技术由于通量有限和/或样品制备复杂而变得日益困难。在这项工作中，我们提出了一种机器学习（ML）辅助的射频（RF）介电光谱细胞术（DiSC）平台，用于微塑料的无标记检测和分类。悬浮在去离子（DI）水中的八种标称直径为10微米的微塑料颗粒在跨越0.2至9 GHz的四个频率下进行了表征。以载液为参考，测量的射频散射参数（S参数）的变化被用于训练有监督的机器学习模型以进行材料分类，包括在混合样品和盐水环境中的微塑料识别。对于悬浮在去离子水中的八种微塑料类别，所提出的方法实现了宏平均F1分数、精确率和召回率均超过0.71。此外，在含有3.3%和6.6%海盐的盐水载液中，PET的分类性能基本得以保持。这些结果证明了机器学习辅助的射频DiSC在水环境中进行快速、单颗粒微塑料分类的可行性。未来的工作将侧重于通过增强射频校准、增加光谱覆盖范围、扩大训练数据集以及使用环境老化和生物污染的微塑料进行验证来提高分类性能。

英文摘要

Micro- and nano-plastic particles (MPs/NPs) are ubiquitous environmental contaminants whose increasing abundance and potential health impacts have created an urgent need for rapid, label-free detection methods. As particle size decreases to the low-micrometer range, conventional optical and spectroscopic techniques become increasingly challenging because of limited throughput and/or complex sample preparation. In this work, we present a machine learning (ML)-assisted radio-frequency (RF) dielectric spectroscopic cytometry (DiSC) platform for the label-free detection and classification of MPs. Eight types of $ 10 $ μm nominal-diameter MP particles suspended in deionized (DI) water were characterized at four frequencies spanning $ 0.2\text{-}9\text{ GHz} $. The measured alterations in RF scattering parameters (S-parameters), referenced to the carrier medium, were used to train supervised ML models for material classification, including the identification of MPs in mixed samples and saline-water environments. For eight MP classes suspended in DI water, the proposed method achieved macro-average F1-score, precision, and recall values exceeding $ 0.71 $. Furthermore, PET classification performance was largely maintained in saline carrier media containing $3.3\% $ and $ 6.6\% $ sea salt. These results demonstrate the feasibility of ML-assisted RF DiSC for rapid, single-particle MP classification in aqueous environments. Future work will focus on improving classification performance through enhanced RF calibration, increased spectral coverage, larger training datasets, and validation using environmentally aged and biologically contaminated microplastics.

发表机构

• Clemson University（克莱姆森大学）

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