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Pretreatment-free SERS sensing of microplastics using a self-attention-based neural network on hierarchically porous Ag foams
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Researchers developed a new sensor platform that can identify six common types of microplastics in environmental samples without the time-consuming separation and pre-treatment steps usually required. The system uses specially designed silver surfaces combined with an artificial intelligence algorithm to analyze the unique chemical fingerprints of different plastics. Faster, cheaper microplastic detection tools like this are essential for monitoring contamination levels in water and food that affect human health.
Low-cost detection systems are needed for the identification of microplastics (MPs) in environmental samples. However, their rapid identification is hindered by the need for complex isolation and pre-treatment methods. This study describes a comprehensive sensing platform to identify MPs in environmental samples without requiring independent separation or pre-treatment protocols. It leverages the physicochemical properties of macroporous-mesoporous silver (Ag) substrates templated with self-assembled polymeric micelles to concurrently separate and analyze multiple MP targets using surface-enhanced Raman spectroscopy (SERS). The hydrophobic layer on Ag aids in stabilizing the nanostructures in the environment and mitigates biofouling. To monitor complex samples with multiple MPs and to demultiplex numerous overlapping patterns, we develop a neural network (NN) algorithm called SpecATNet that employs a self-attention mechanism to resolve the complex dependencies and patterns in SERS data to identify six common types of MPs: polystyrene, polyethylene, polymethylmethacrylate, polytetrafluoroethylene, nylon, and polyethylene terephthalate. SpecATNet uses multi-label classification to analyze multi-component mixtures even in the presence of various interference agents. The combination of macroporous-mesoporous Ag substrates and self-attention-based NN technology holds potential to enable field monitoring of MPs by generating rich datasets that machines can interpret and analyze.
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Interfacial Regulation–Driven Dual-Enrichment SERS Coupled With Deep Learning Enable Ultrasensitive and In Situ Identification of Microplastics in Natural Waters
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Scientists have developed a new sensor technology that can detect tiny microplastic particles in real water samples—like rivers or tap water—at extremely low levels, without needing complex lab processing. By combining specially designed metal nanoparticles with AI-powered pattern recognition, the tool can also tell different types of plastic apart, even when they're mixed together. This matters because better detection tools are a key first step toward understanding how much plastic pollution is actually in our water supplies and food chain, which is important for assessing potential risks to human health.
Deep Learning-Aided SERS Detection of Microplastics in Water Samples with a Hierarchically Porous Gold Sponge Substrate
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Researchers built a hierarchically porous gold sponge SERS substrate paired with a modular binary convolutional neural network, enabling pretreatment-free identification of five microplastic polymer types from complex water samples with 98.96% precision, and used gradient-weighted activation mapping to confirm that the model learned chemically meaningful Raman features rather than spurious correlations.
Interfacial Regulation–Driven Dual‐Enrichment SERS Coupled With Deep Learning Enable Ultrasensitive and In Situ Identification of Microplastics in Natural Waters (Small 44/2026)
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Scientists have developed a new test kit that can detect tiny amounts of microplastics in natural water sources quickly and without complicated lab prep, using special light-sensing nanoparticles and AI to identify different plastic types. This matters because microplastics are increasingly found in our drinking water and food chain, and having a fast, sensitive detection tool could help researchers and water treatment facilities catch contamination early, an important step toward understanding and reducing our exposure to these particles.
Integrating MetalAquaDect SERS platform: Machine-learning assisted real-time monitoring of sub-2mg/L microplastics and nanoplastics in complex matrices
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Researchers used a machine learning-assisted SERS platform (AquaDect) to qualitatively and quantitatively detect microplastics and nanoplastics of multiple types and sizes in aqueous solutions at concentrations below 2 mg/L, demonstrating the approach across polystyrene, polyethylene, polypropylene, and PMMA.
Toward Nano- and Microplastic Sensors: Identification of Nano- and Microplastic Particles via Artificial Intelligence Combined with a Plasmonic Probe Functionalized with an Estrogen Receptor
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Scientists created a sensor that combines artificial intelligence with a specialized light-based probe to detect and identify different types of nano- and microplastics in water. The AI-powered system could distinguish between various plastic types with high accuracy, offering a faster and more practical way to monitor plastic contamination in drinking water and environmental samples.
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