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Synergistically Enhanced Ta2O5/AgNPs SERS Substrate Coupled with Deep Learning for Ultra-Sensitive Microplastic Detection
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Researchers engineered a high-performance Ta2O5/AgNPs composite surface-enhanced Raman scattering (SERS) substrate and coupled it with deep learning algorithms for ultra-sensitive detection of microplastics. Through morphology modulation and band-gap engineering of the semiconductor support, the system achieved significantly enhanced Raman signal amplification, enabling identification of microplastics at very low concentrations.
Herein, a high-performance Ta2O5/AgNPs composite Surface-Enhanced Raman Scattering (SERS) substrate is engineered for highly sensitive detection of microplastics. Through morphology modulation and band-gap engineering, the semiconductor Ta2O5 is structured into spheres and composited with silver nanoparticles (AgNPs), facilitating efficient charge transfer and localized surface plasmon resonance (LSPR). This architecture integrates electromagnetic (EM) and chemical (CM) enhancement mechanisms, achieving an ultra-low detection limit of 10-13 M for rhodamine 6G (R6G) with excellent linearity. Furthermore, the three-dimensional "pseudo-Neuston" network structure exhibits superior capture capability for microplastics (PS, PET, PMMA). To address spectral interference in simulated complex environments, a multi-scale deep-learning model combining wavelet transform, Convolutional Neural Networks (CNN), and Transformers is proposed. This model achieves a classification accuracy of 98.7% under high-noise conditions, significantly outperforming traditional machine learning methods. This work presents a robust strategy for environmental monitoring, offering a novel solution for precise risk assessment of microplastic pollution.
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Superhydrophobic Surface-Enhanced Raman Spectroscopy (SERS) Substrates for Sensitive Detection of Trace Nanoplastics in Water
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Researchers developed a new method to detect extremely small nanoplastics in water by combining a water-repelling surface that concentrates particles with a technique called SERS that amplifies their chemical signal. The method can identify common nanoplastics like polystyrene and PMMA at very low concentrations, which is an important step toward monitoring these tiny pollutants that are difficult to detect with current tools.
Advanced microplastic monitoring using Raman spectroscopy with a combination of nanostructure-based substrates
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Researchers reviewed advances in Raman spectroscopy and surface-enhanced Raman scattering (SERS) — a technique that amplifies light signals using metallic nanostructures — for detecting micro- and nanoplastics at trace concentrations in environmental samples, highlighting new plasmonic materials, 3D substrates, and microfluidic chip platforms that enable on-site monitoring.
Highly sensitive superhydrophobic SERS substrate combined with machine learning for precise identification and classification of nanoplastics
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Researchers fabricated a superhydrophobic surface-enhanced Raman scattering (SERS) substrate that concentrates nanoplastics in a tiny detection zone, then combined it with machine learning to identify seven types of nanoplastics in real lake water with 99.88% accuracy, offering a practical high-throughput environmental monitoring approach.
High-sensitivity SERS sensor leveraging three-dimensional Ti3C2Tx/TiO2/W18O49 semiconductor heterostructures for reliable detection of trace micro/nanoplastics in environmental matrices
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Researchers developed a new sensor that can detect trace amounts of micro- and nanoplastics in environmental samples like rainwater, soil, and wastewater. The sensor uses a layered semiconductor structure to enhance Raman spectroscopy signals, achieving high sensitivity and the ability to identify multiple plastic types at once. This technology could make it faster and more practical to monitor plastic pollution in real-world settings.
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.
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