Gong Liyuan, Varela Bryan, Eskandari Erfan, Lombana Juan Zubieta, Biswas Payel, Ma Luyao, Andreu Irene, Lin Yang
Department of Mechanical, Industrial and Systems Engineering, College of Engineering, University of Rhode Island, Kingston, RI 02881, United States.
Department of Chemical Engineering, College of Engineering, University of Rhode Island, Kingston, RI 02881, United States.
J Hazard Mater. 2025 Aug 15;494:138472. doi: 10.1016/j.jhazmat.2025.138472. Epub 2025 May 1.
The rising presence of nanoplastics in water poses toxicity risks and long-term ecological and health impacts. Detecting nanoplastics remains challenging due to their small size, complex chemistry, and environmental interference. Traditional filtration combined with Raman spectroscopy is time-consuming, labor-intensive, and often lacks accuracy and sensitivity. This study presents an agarose-based microfiltration device integrated with machine learning-assisted Raman analysis for nanoplastic capture and identification. The 1 % agarose microfluidic channel features circular micropost arrays enabling dual filtration: nanoplastics diffuse into the porous matrix, while larger particles (>1000 nm) are blocked by the microposts. Unlike conventional systems, this design achieves both physical separation and preconcentration, enhancing nanoplastic detectability. Upon dehydration, the agarose forms a transparent film, significantly improving Raman compatibility by minimizing background interference. This transformation enables direct Raman analysis of retained nanoparticles with enhanced signal clarity and sensitivity. Using 100-nm polystyrene nanoparticles (PSNPs) as a model, we evaluated device performance in distilled water and seawater across concentrations (6.25-50 µg/mL) and flow rates (2.5-100 µL/min). Maximum capture efficiencies of 80 % (seawater) and 66 % (distilled water) were achieved at 2.5 µL/min. A convolutional neural network (CNN) further enhanced spectral analysis, reducing mapping time by 50 % and enabling PSNP detection in seawater at 6.25 µg/mL. This agarose-based system offers a scalable, cost-effective platform for nanoplastic sampling, demonstrating the potential of combining microfluidics with machine learning-assisted Raman spectroscopy to address critical environmental and public health challenges.
水中纳米塑料的不断增多带来了毒性风险以及长期的生态和健康影响。由于纳米塑料尺寸小、化学性质复杂且存在环境干扰，对其进行检测仍然具有挑战性。传统的过滤与拉曼光谱相结合的方法既耗时又费力，而且常常缺乏准确性和灵敏度。本研究提出了一种基于琼脂糖的微滤装置，该装置集成了机器学习辅助拉曼分析技术，用于纳米塑料的捕获和识别。1%琼脂糖微流控通道具有圆形微柱阵列，可实现双重过滤：纳米塑料扩散到多孔基质中，而较大的颗粒（>1000 nm）则被微柱阻挡。与传统系统不同，这种设计实现了物理分离和预浓缩，提高了纳米塑料的可检测性。脱水后，琼脂糖形成透明薄膜，通过最小化背景干扰显著提高了拉曼兼容性。这种转变使得能够对保留的纳米颗粒进行直接拉曼分析，信号清晰度和灵敏度得到增强。以100 nm的聚苯乙烯纳米颗粒（PSNPs）为模型，我们评估了该装置在蒸馏水和海水中不同浓度（6.25 - 50 μg/mL）和流速（2.5 - 100 μL/min）下的性能。在2.5 μL/min时，海水和蒸馏水的最大捕获效率分别达到80%和66%。卷积神经网络（CNN）进一步增强了光谱分析，将映射时间减少了50%，并能够在海水中检测到浓度为6.25 μg/mL的PSNP。这种基于琼脂糖的系统为纳米塑料采样提供了一个可扩展、经济高效的平台，证明了将微流控技术与机器学习辅助拉曼光谱相结合以应对关键环境和公共卫生挑战的潜力。
