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Papers
61,005 resultsShowing papers similar to 3D Plasmonic Gold Nanopocket Structure for SERS Machine Learning‐Based Microplastic Detection
ClearMachine learning-integrated droplet microfluidic system for accurate quantification and classification of microplastics
Scientists developed a new microplastic detection system that combines tiny droplet-based testing with machine learning to quickly identify and classify microplastic particles. This portable system can accurately detect microplastics on-site without expensive lab equipment, which could make widespread environmental and food safety monitoring much more practical.
A gold nanoparticle doped flexible substrate for microplastics SERS detection
Researchers developed a gold nanoparticle-doped filter paper as a flexible substrate for detecting microplastics using surface-enhanced Raman scattering. The method achieved a minimum detectable concentration of 0.1 grams per liter for PET in water and was successfully validated by detecting microplastics in tap water and pond water samples.
Plasmonic filter paper for microplastic detection: SERS enhancement, size dependence, and quantitative limitations
Researchers fabricated SERS-active gold-coated filter paper substrates and evaluated their performance for detecting microplastics, finding that SERS signal strength depends significantly on particle size and that the technique has inherent limitations for quantitative analysis of microplastic concentrations.
Pretreatment-free SERS sensing of microplastics using a self-attention-based neural network on hierarchically porous Ag foams
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.
Field-Deployable Plasmonic Sensing and Machine Learning Classification of Microplastics Using Peptide–AuNP Conjugates
Researchers developed a portable peptide-gold nanoparticle assay that converts polymer-specific interactions into a colorimetric signal detectable by machine learning, enabling field-deployable classification of common microplastic types in water without laboratory equipment.
Microplastic detection and recognition system enabled by a triboelectric nanogenerator and machine learning techniques
Researchers developed a simple, rapid microplastic detection and identification device combining liquid-solid contact electrification with machine learning algorithms. The system could distinguish between different types of microplastics in water based on open-circuit voltage differences, offering a lower-cost and faster alternative to conventional detection methods.
Detection of Microplastics Based on a Liquid–Solid Triboelectric Nanogenerator and a Deep Learning Method
Scientists developed a new microplastic detection device based on a liquid-solid friction generator combined with deep learning AI to identify different types of plastic particles. The system can classify microplastics by material type with high accuracy using electrical signals generated when plastic particles contact a liquid surface. This technology could make it easier and cheaper to monitor microplastic contamination in water supplies.
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.
Machine learning-driven optical microfiltration device for improved nanoplastic sampling and detection in water systems
Researchers developed a new device combining agarose-based microfiltration with machine learning-assisted Raman spectroscopy to detect nanoplastics in water more accurately and efficiently. The system achieved over 96% accuracy in identifying nanoplastic particles while dramatically reducing analysis time compared to traditional methods. Better detection tools like this are essential for monitoring nanoplastic levels in drinking water and assessing risks to human health.
Plastic analysis with a plasmonic nano-gold sensor coated with plastic binding peptides.
This study describes a sensor technology using gold nanoparticles coated with plastic-binding peptides to detect and identify small plastic particles in the environment. Developing rapid, accurate detection methods is a critical step toward understanding how much microplastic contamination exists in water and other environments, and this approach offers a potentially faster and more sensitive alternative to conventional identification techniques.
Integrating MetalAquaDect SERS platform: Machine-learning assisted real-time monitoring of sub-2mg/L microplastics and nanoplastics in complex matrices
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.
Droplet-based Opto-microfluidic Device for Microplastic Sensing in Aqueous Solutions
Researchers developed a microfluidic device using light to detect plastic microspheres in water droplets, offering a new tool for identifying microplastic contamination in aquatic environments.
Artificial Intelligence-Based Microfluidic Platform for Detecting Contaminants in Water: A Review
This review explores how microfluidic devices combined with artificial intelligence can detect water pollutants including microplastics and nanoplastics in real-time, outside the laboratory. Traditional water testing requires large lab equipment, but these portable chip-based systems can identify contaminants quickly and accurately using machine learning. This technology could improve monitoring of microplastic contamination in drinking water and other water sources.
Controllable preparation of mesoporous spike gold nanocrystals for surface-enhanced Raman spectroscopy detection of micro/nanoplastics in water
Researchers developed a novel detection method combining membrane filtration and surface-enhanced Raman spectroscopy (SERS) using specially synthesized spiked gold nanocrystals to detect nanoplastics in water. The method can simultaneously enrich and detect nanoplastic particles as small as 20 nanometers, addressing a significant gap in reliable detection techniques for these small plastic contaminants that have been found in human blood and placenta.
A Highly Sensitive SERS Substrate for Detection of Nanoplastics in Water
Researchers developed a highly sensitive SERS-based substrate for detecting nanoplastic particles in water at very low concentrations. Improved detection tools for nanoplastics are essential for monitoring their presence in drinking water and understanding exposure risks to human health.
Toward Nano- and Microplastic Sensors: Identification of Nano- and Microplastic Particles via Artificial Intelligence Combined with a Plasmonic Probe Functionalized with an Estrogen Receptor
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.
Cost-Effective and Wireless Portable Device for Rapid and Sensitive Quantification of Micro/Nanoplastics
Researchers developed a wireless portable device for rapid quantification of micro- and nanoplastics in water samples, offering a field-deployable alternative to laboratory-based analysis for environmental monitoring.
A simple and rapid preparation of Au-Ag alloy nanourchins flexible membrane for ultrasensitive SERS detection of microplastics in water environment
Researchers fabricated flexible gold-silver alloy nanourchins on a membrane substrate and demonstrated their use as a SERS sensor for rapid, sensitive detection of microplastics in water, achieving detection of multiple polymer types at low concentrations without complex sample preparation.
Microfluidic Detection and Analysis of Microplastics Using Surface Nanodroplets
Researchers developed a microfluidic device that uses tiny surface droplets to capture and analyze microplastics as small as 10 micrometers from water samples. The captured particles can be examined under a microscope and identified by type using Raman spectroscopy without removing them from the device. The method offers a simpler, faster, and more affordable way to detect small microplastics compared to conventional filtration techniques.
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.
A Low-Cost Microfluidic Method for Microplastics Identification: Towards Continuous Recognition
Researchers developed a low-cost 3D-printed microfluidic device combining Nile Red staining with continuous-flow processing to enable rapid, affordable microplastic identification, demonstrating performance comparable to conventional staining methods while supporting field-deployable 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.
A green approach to nanoplastic detection: SERS with untreated filter paper for polystyrene nanoplastics
Researchers developed a simple and affordable method to detect nanoplastics in water using silver nanoparticles and ordinary filter paper, achieving detection of polystyrene particles as small as 100 nanometers. The method successfully identified nanoplastics in both drinking water and tap water samples. Better detection tools like this are important because they make it easier to monitor nanoplastic contamination in the water people actually drink, helping researchers understand real-world exposure levels.
High-throughput microplastic assessment using polarization holographic imaging
Researchers built a portable, low-cost system that uses holographic imaging and polarized light combined with deep learning to automatically detect, count, and classify microplastics in water in real time — without lengthy sample preparation. This tool significantly speeds up microplastic monitoring and could be widely deployed for environmental surveillance.