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Papers
61,005 resultsShowing papers similar to Synergistically Enhanced Ta2O5/AgNPs SERS Substrate Coupled with Deep Learning for Ultra-Sensitive Microplastic Detection
ClearSuperhydrophobic Surface-Enhanced Raman Spectroscopy (SERS) Substrates for Sensitive Detection of Trace Nanoplastics in Water
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
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
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
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.
Trapping tiny pollutants: SERS-driven strategies for microplastics and nanoplastics detection
This review explores how surface-enhanced Raman spectroscopy (SERS) is being developed as a highly sensitive tool for detecting and identifying micro- and nanoplastics in environmental and biological samples. Researchers highlight recent advances in sensor design, the integration of machine learning for improved accuracy, and the technique's potential for real-world monitoring. The study also identifies key challenges, including signal variability and the lack of standardized methods, that need to be resolved for broader adoption.
Detection and classification of microplastics in green tea using SERS with gold nanoparticle substrates integrating chemometrics and deep learning
Researchers developed a method using surface-enhanced Raman scattering with gold nanoparticle substrates to detect and classify polystyrene and PET microplastic contamination in green tea powders. They compared chemometric and deep learning classification approaches, finding that partial least squares discriminant analysis achieved the highest accuracy at 100% for most tea varieties. The method offers a practical tool for detecting microplastic contamination in food products.
Component identification for the SERS spectra of microplastics mixture with convolutional neural network
Researchers developed a convolutional neural network that identified microplastic components in mixed surface-enhanced Raman spectroscopy samples with 99.54% accuracy, outperforming traditional methods without requiring spectral preprocessing.
Thermoelectrically Driven Dual-Mechanism Regulation on SERS and Application Potential for Rapid Detection of SARS-CoV-2 Viruses and Microplastics
Researchers developed a highly sensitive thermoelectrically driven surface-enhanced Raman scattering (SERS) substrate for detecting trace-level contaminants. The study demonstrated that applying a temperature gradient to the substrate dramatically enhanced detection sensitivity, with potential applications for rapid identification of microplastics and other environmental contaminants at very low concentrations.
Integrating Metal Phenolic Networks-Mediated Separation and Machine Learning-Aided SERS for High-Precision Quantification and Classification of Nanoplastics
Scientists combined metal-phenolic network chemistry — which rapidly concentrates and captures nanoplastics — with machine-learning-enhanced surface-enhanced Raman spectroscopy (SERS) to accurately identify and quantify nanoplastics at very low environmental concentrations. This integrated approach addresses one of the biggest technical obstacles in nanoplastic research: detecting particles that are too small and too sparse for conventional methods to reliably find.
Advances in Surface‐Enhanced Raman Spectroscopy for Detection of Aquatic Environmental Pollutants
This review examines surface-enhanced Raman scattering (SERS) as a technique for detecting aquatic pollutants, highlighting its exceptional sensitivity and molecular fingerprinting capability for identifying microplastics and other contaminants at trace concentrations.
Latest Advances and Developments to Detection of Micro‐ and Nanoplastics Using Surface‐Enhanced Raman Spectroscopy
This review examines the latest developments in using surface-enhanced Raman spectroscopy (SERS) to detect micro- and nanoplastics in various environmental samples. Researchers found that SERS offers significantly improved sensitivity compared to conventional methods, enabling detection of smaller plastic particles. The study suggests that SERS-based approaches hold promise for advancing nanoplastic detection, though challenges around standardization and reproducibility remain.
A review of recent progress in the application of Raman spectroscopy and SERS detection of microplastics and derivatives
This review covers advances in using Raman spectroscopy and surface-enhanced Raman spectroscopy (SERS) to detect and identify microplastics in the environment. These techniques offer high resolution and sensitive detection that can identify specific plastic types even at very small sizes. Better detection methods are essential for understanding the true extent of microplastic contamination and its potential risks to human health.
Fabrication of Bowl Array Surface-Enhanced Raman Scattering Substrates via Ag Nanoparticle Self-Assembly on Polymer UV-Imprinted Microbowls for Enhanced Raman Detection of Microplastics
Researchers fabricated bowl-array surface-enhanced Raman scattering substrates by depositing silver nanoparticles via self-assembly onto UV-imprinted polymer microbowls, creating 50-micrometre diameter bowl structures that combine SERS enhancement with light-trapping to enable highly sensitive detection of micrometer-sized microplastics.
Integrating Metal–Phenolic Networks-Mediated Separation and Machine Learning-Aided Surface-Enhanced Raman Spectroscopy for Accurate Nanoplastics Quantification and Classification
Researchers combined a metal-based separation technique with machine learning and surface-enhanced Raman spectroscopy to detect and classify nanoplastics in environmental samples. The method achieved high accuracy in identifying different types of nanoplastics at very low concentrations. This approach could make it significantly easier and more reliable to monitor nanoplastic contamination in real-world water and soil samples.
Detection of Polystyrene Microplastics up to the SingleNanoparticle Limit Using SERS and Advanced ANN Design (KANformer)
Researchers developed a surface-enhanced Raman spectroscopy (SERS) platform combined with a KANformer neural network to detect polystyrene microplastics down to the single nanoparticle level, offering a highly sensitive monitoring tool for environmental plastic contamination.
In situ surface-enhanced Raman spectroscopy for detecting microplastics and nanoplastics in aquatic environments
This study evaluated surface-enhanced Raman spectroscopy (SERS) as a method for detecting and identifying microplastics and nanoplastics in aquatic environments, demonstrating its potential for detecting particles too small for conventional spectroscopy while noting remaining challenges for field deployment.
On-Site Detection of Nanoplastics in Liquid Phase by SERS Method
Researchers developed an on-site detection method for nanoplastics in liquid samples using surface-enhanced Raman spectroscopy (SERS), achieving sensitive identification without the laboratory infrastructure required by conventional GC-MS approaches. The SERS method successfully differentiated nanoplastic types in environmental water samples, offering a practical tool for rapid field-deployable nanoplastic monitoring.
Strategies and Challenges of Identifying Nanoplastics in Environment by Surface-Enhanced Raman Spectroscopy
Researchers reviewed the use of surface-enhanced Raman spectroscopy (SERS) as a tool for detecting nanoplastics, which are plastic particles smaller than one micrometer. The study found that SERS offers high sensitivity for identifying individual nanoparticles, but significant challenges remain in applying this technique to complex environmental samples. The review outlines strategies for improving SERS-based nanoplastic detection to better assess environmental and health risks.
Meniscus‐Confined 3D Printed Nanoparticles: A Comparative Study of Quantitative SERS Detection of Microplastics
Detecting microplastics accurately in environmental samples is technically challenging, and this study introduces a new approach using 3D-printed silver and gold nanoparticle surfaces that amplify the light signal from microplastics when analyzed by Raman spectroscopy. Both types of printed substrates could detect plastic particles at concentrations as low as 0.3–1.2 micrograms per milliliter, with high reproducibility across dozens of repeated measurements. This technology could make routine, sensitive microplastic monitoring faster and more practical for environmental agencies and researchers.
Honeycomb-like AgNPs@TiO2 array SERS sensor for the quantification of micro/nanoplastics in the environmental water samples
Researchers developed a honeycomb-like silver nanoparticle and titanium dioxide array sensor using surface-enhanced Raman scattering for detecting micro- and nanoplastics in environmental water. The sensor could identify polystyrene microplastics at concentrations as low as 100 micrograms per milliliter across tap water, lake water, soil water, and seawater, with recovery rates ranging from 97.6% to 109.7%.
Detection of Polystyrene Microplastics up to the Single Nanoparticle Limit Using SERS and Advanced ANN Design (KANformer)
Researchers developed a new detection method that can identify a single polystyrene nanoparticle using surface-enhanced Raman spectroscopy combined with an advanced machine learning algorithm. By heating microplastic samples to melt them into plasmonic hot spots on a sensor surface, they overcame a key limitation of previous detection approaches. The technique also works for other common plastics like polyethylene and polypropylene, offering a powerful tool for monitoring microplastic contamination at extremely low concentrations.
Co-Self-Assembled Monolayer Enables Sensitive SERS Detection of Nanoplastics via Spontaneous Hotspot Entrapment
Researchers developed a new detection method that can identify and measure nanoplastics at concentrations as low as 0.01 micrograms per milliliter by trapping the tiny particles within a single layer of silver nanoparticles. The technique uses surface-enhanced Raman scattering, which amplifies the chemical signal of nanoplastics that are spontaneously captured in the detection hotspots. This approach offers a faster and more sensitive way to monitor nanoplastic pollution in water compared to existing methods.
Advancing SERS-based detection of micro and nanoplastics in Agroecosystems: Current progress, challenges, and future directions
This review examines the potential of surface-enhanced Raman spectroscopy (SERS) as a point-of-care detection tool for micro- and nanoplastics in agroecosystems, highlighting its sensitivity advantages over conventional methods. It covers SERS substrate design, pre-treatment strategies, and recent applications in soil and plant matrices.
Portable surface-enhanced Raman scattering platform for rapid identification of nanoplastics at single-particle level
Researchers developed a portable, gold-nanoparticle-coated paper substrate for surface-enhanced Raman scattering (SERS) that detects individual plastic particles down to 1 part per trillion, enabling rapid field identification of polystyrene and nylon nanoplastics released from food containers and teabags without laboratory equipment.