Kukralova Karolina, Trelin Andrii, Miliutina Elena, Burtsev Vasilii, Svorcik Vaclav, Lyutakov Oleksiy
Department of Solid State Engineering, University of Chemistry and Technology, Prague 16628, Czech Republic.
ACS Sens. 2025 Jul 25;10(7):4983-4995. doi: 10.1021/acssensors.5c00846. Epub 2025 Jun 25.
Due to uncontrolled release, gradual accumulation, low degradation rate, and potential negative impact on human health, microplastics (MPs) pose a serious environmental and healthcare risk. Thus, the spread of MPs should be at least carefully monitored to identify and eliminate their main sources, as well as to provide a suitable alarm in the case of MP concentration increase. Among various detection methods, surface-enhanced Raman spectroscopy (SERS) poses a unique detection limit and the ability to perform outdoor measurements without preliminary sample treatment. However, the utilization of SERS for MPs detection is significantly limited for a few reasons. First, the maximal SERS enhancement occurs in the so-called hot spots, where the MPs cannot penetrate due to their size. In addition, the natural environment can produce a significant spectral background, which blocks the microplastic characteristic signal. To overcome these limitations, we propose a new alternative route for introduction of MPs into the plasmonic hot spots, using in situ MP annealing and an advanced artificial neural network (ANN) design, the Kolmogorov-Arnold transformer (KANformer, KANF). Polystyrene (PS) MPs were used as a model compound. We have also demonstrated the potential versatility of our approach using different microplastics, such as polyethylene, polypropylene, and polyethylene terephthalate. The proposed approach allows us to detect the presence of PS up to the single nanoparticle limit (in the mL of analyzed solution) with a probability of above 95%, even under mixing with groundwater model matrices.
由于微塑料（MPs）的无控释放、逐渐积累、降解率低以及对人类健康的潜在负面影响，它们构成了严重的环境和健康风险。因此，至少应仔细监测微塑料的扩散情况，以识别和消除其主要来源，并在微塑料浓度增加时发出适当警报。在各种检测方法中，表面增强拉曼光谱（SERS）具有独特的检测限，并且能够在无需对样品进行预处理的情况下进行户外测量。然而，由于一些原因，SERS在微塑料检测中的应用受到显著限制。首先，最大的SERS增强发生在所谓的热点区域，而微塑料由于其尺寸无法进入这些区域。此外，自然环境会产生显著的光谱背景，这会阻断微塑料的特征信号。为了克服这些限制，我们提出了一种将微塑料引入等离子体热点的新方法，即使用原位微塑料退火和先进的人工神经网络（ANN）设计——柯尔莫哥洛夫 - 阿诺德变换器（KANformer，KANF）。以聚苯乙烯（PS）微塑料作为模型化合物。我们还使用不同的微塑料，如聚乙烯、聚丙烯和聚对苯二甲酸乙二酯，展示了我们方法的潜在通用性。即使在与地下水模型基质混合的情况下，所提出的方法也能让我们以高于95%的概率检测到低至单纳米颗粒极限（在所分析溶液的毫升数中）的PS的存在。
