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Papers
61,005 resultsShowing papers similar to Field-Deployable Plasmonic Sensing and Machine Learning Classification of Microplastics Using Peptide–AuNP Conjugates
ClearGold nanoparticles-anchored peptides enable precise colorimetric estimation of microplastics
Researchers developed a colorimetric method for detecting microplastics using gold nanoparticle-anchored peptides that selectively bind plastic surfaces, enabling precise and rapid estimation of microplastic contamination in aquatic environments.
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.
Identification of Polymeric Nanoparticles Using StrategicPeptide Sensor Configurations and Machine Learning
Researchers identified polymeric nanoparticles in water using a peptide sensor array combined with machine learning, demonstrating that this approach could distinguish between different nanoplastic types without requiring specialized optical instruments.
Identification of Polymeric Nanoparticles Using Strategic Peptide Sensor Configurations and Machine Learning
Researchers created a sensor system using specially designed peptides combined with machine learning to identify different types of plastic nanoparticles dispersed in water. The peptide sensors produced distinct fluorescence patterns for each polymer type, and the AI algorithms could accurately distinguish between plastics with very similar chemical structures. This approach could help fill a critical gap in environmental monitoring, since detecting nanoplastics in water remains a major challenge with current technology.
Plastic Analysis with a Plasmonic Nano-Gold Sensor Coated with Plastic-Binding Peptides
Researchers developed a nano-gold sensor coated with plastic-binding peptides to detect common plastic polymers including polyethylene, PET, polypropylene, and polystyrene at very small scales. When tested on freshwater mussels deployed at suspected pollution sites, the sensor detected higher plastic levels near rainfall overflow and urban areas compared to treated municipal effluent sites.
Selective on-site detection and quantification of polystyrene microplastics in water using fluorescence-tagged peptides and electrochemical impedance spectroscopy
Researchers created a portable detection system using fluorescence-tagged peptides and electrochemical sensors to identify polystyrene microplastics in different water types. The method could detect microplastics across a wide size range and in various water conditions, including seawater and tap water. This on-site detection approach could make microplastic monitoring faster and more accessible compared to traditional laboratory methods.
Identification of Water-Soluble Polymers through Discrimination of Multiple Optical Signals from a Single Peptide Sensor
Researchers developed a single peptide-based sensor that can identify different water-soluble synthetic polymers in water by detecting patterns of optical signals. Water-soluble plastics are an emerging pollution concern in wastewater. This type of sensor could help monitor plastic polymer contamination in water systems where conventional microplastic detection methods may not work.
3D Plasmonic Gold Nanopocket Structure for SERS Machine Learning‐Based Microplastic Detection
Researchers developed a new paper-based detection system that uses gold nanostructures and machine learning to quickly identify microplastics in water samples. The device works like a filter and sensor combined, capturing microplastics and identifying their type without complex sample preparation. This portable technology could make it much easier to test drinking water and environmental samples for microplastic contamination on-site.
Gold nanoparticle assisted colorimetric biosensors for rapid polyethylene terephthalate (PET) sensing for sustainable environment to monitor microplastics
Researchers proposed a gold nanoparticle-based colorimetric biosensor for rapid detection of polyethylene terephthalate microplastics, using computational modeling to evaluate PET-binding peptides that could enable simple, field-deployable microplastic monitoring.
Peptide-based strategies for detecting microplastics in aquatic systems: A review
This review explores the emerging use of specially designed peptides that can bind to specific types of plastic for detecting microplastics in water. Researchers describe how advances in protein engineering and computational design have enabled the creation of peptides that selectively recognize different polymer surfaces. The peptide-based approach offers a promising new detection method that could complement existing techniques for monitoring microplastic pollution in aquatic environments.
Design and Development of an Advanced Sensor Prototype for the Detection of Microplastics
Researchers designed and developed an advanced sensor prototype for detecting microplastics in water, combining spectroscopic and signal processing technologies into a portable device. The prototype demonstrated accurate microplastic identification across multiple polymer types in field conditions.
Ms.
Researchers developed a low-cost, sustainable detection system using polymer-specific, high-affinity binding peptides for identifying and sorting microplastics by polymer type in mixed heterogeneous samples. The approach leverages peptide-based molecular recognition as an alternative to expensive spectroscopic methods, aiming to improve microplastic detection and facilitate recycling processes by enabling polymer-type discrimination in complex waste streams.
Raman Spectroscopy and Machine Learning for Microplastics Identification and Classification in Water Environments
Researchers combined Raman spectroscopy with machine learning algorithms for automated identification and classification of microplastics in water environments, achieving high accuracy in distinguishing different polymer types based on spectral fingerprints.
Rapid identification of microplastic using portable Raman system and extra trees algorithm
Researchers developed a portable Raman spectroscopy system combined with a machine learning algorithm to rapidly identify and classify different types of microplastics in the field. Portable real-time identification tools are important for environmental monitoring programs that need to quickly characterize microplastics without sending samples to a laboratory.
Identification of Water-Soluble Polymers through Machine Learning of Fluorescence Signals from Multiple Peptide Sensors
Researchers developed a chemical tongue system using multiple fluorescently responsive peptide sensors combined with supervised and unsupervised machine learning to identify water-soluble synthetic polymers, demonstrating that fluorescence spectra patterns from peptide-polymer mixtures provide sufficient discriminatory information for accurate polymer identification.
Classification of Microplastic Particles in Water using Polarized Light Scattering and Machine Learning Methods
Researchers developed a reflection-based, in-situ classification method for microplastic particles in water using polarized light scattering combined with machine learning, successfully identifying colorless particles in the 50-300 micrometer range. The approach circumvents transmission-based interference problems and offers a pathway toward continuous, large-scale microplastic monitoring in aquatic environments.
Field-Portable Microplastic Sensing in Aqueous Environments: A Perspective on Emerging Techniques
This review examines emerging field-portable technologies for detecting and quantifying microplastics in aqueous environments, discussing optical, spectroscopic, and electrochemical sensing approaches. Researchers identify the lack of a standardized, rapid on-site method as the primary bottleneck limiting accurate real-world microplastic monitoring.
Identification of Pristine and Protein Corona Coated Micro- and Nanoplastic Particles with a Colorimetric Sensor Array
Scientists developed a low-cost colorimetric sensor array — essentially a panel of color-changing dyes — capable of detecting and distinguishing different types of micro- and nanoplastics at concentrations as low as 10 nanograms per milliliter. The tool can also differentiate between bare plastic particles and those coated with proteins (as would happen in a biological environment), making it a promising rapid-screening method for microplastic monitoring in water.
Toward in Situ Identification of Microplastics in Water Using Raman Spectroscopy and Machine Learning
This study developed an early-stage system combining Raman spectroscopy and machine learning to identify microplastics directly in ocean water in real time, without needing to collect and process samples in a lab. A support vector machine classifier trained on spectral libraries correctly identified all pristine microplastic samples and most environmental ones, demonstrating that field-deployable automated detection is feasible. Accurate real-time monitoring tools are urgently needed to understand where microplastics concentrate in the ocean and to track pollution trends.
Machine 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.
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.
Plastibodies for multiplexed detection and sorting of microplastic particles in high-throughput
Researchers developed a high-throughput flow cytometry method using material-binding peptide antibodies (plastibodies) for multiplexed detection and sorting of microplastic particles, enabling sensitive and rapid quantification in aqueous samples.
A Portable Optical Sensor for Microplastic Detection: Development and Calibration
Researchers built a portable, low-cost optical sensor prototype designed to detect microplastics by shining multiple wavelengths of light through water samples. The device measures how different plastic particles absorb and scatter light, producing color spectra that can help identify microplastics. The sensor offers an affordable field-deployable option for environmental monitoring, with potential future improvements using machine learning for automated identification.
Ms.
Researchers developed polymer-specific, high-affinity binding peptides capable of selectively identifying microplastic types in mixed environmental samples, aiming to create a rapid and cost-effective alternative to conventional spectroscopic identification methods. The approach leverages peptide-surface interactions to discriminate between different plastic polymers, addressing the challenge of accurately detecting microplastics released through plastic degradation and industrial processing.