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Papers
61,005 resultsShowing papers similar to Identification of Polymeric Nanoparticles Using Strategic Peptide Sensor Configurations and Machine Learning
ClearIdentification 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.
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.
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.
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.
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.
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.
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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.
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.
Biophysics-guided uncertainty-aware deep learning uncovers high-affinity plastic-binding peptides
Researchers used artificial intelligence combined with biophysical modeling to discover new peptides (short protein fragments) that bind tightly to common plastics like polyethylene, polypropylene, and polystyrene. These plastic-binding peptides could be used to detect or capture microplastics in the environment using biodegradable materials. The technology represents a promising new approach to cleaning up microplastic pollution.
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Researchers developed polymer-specific, high-affinity binding peptides covalently linked to fluorescent probes to create a low-cost, sustainable detection system capable of identifying and labelling specific plastic polymers in mixed environmental and industrial microplastic samples. The method aimed to enable fast, accurate polymer identification in heterogeneous samples relevant to both recycling and environmental monitoring applications.
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.
De Novo Design of Multiple Microplastic-Binding Peptides with a Protein Language Model-Guided Generative Adversarial Network
Researchers used artificial intelligence to design new peptides that can bind to multiple types of microplastics simultaneously, addressing the challenge that real-world plastic pollution involves many different plastic types. Their AI framework combined protein language models with generative networks to create peptides that showed strong binding affinity to polystyrene, polyethylene, and polypropylene in laboratory tests. The technology could lead to new eco-friendly tools for detecting or capturing microplastic pollution from the environment.
MagNanoTrap enrichment empowers ultra-sensitive quantification of mixed nanoplastic particles from environmental water samples
Researchers developed the MagNanoTrap platform — magnetic nanoparticles coated with a bifunctional peptide — to enrich and quantify nanoplastic particles from environmental water samples, achieving ultra-sensitive detection across multiple polymer types that eluded conventional methods.
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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.
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.
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.
De Novo Design of Multiple Microplastic-Binding Peptideswith a Protein Language Model-Guided Generative Adversarial Network
Researchers used a protein language model combined with a generative adversarial network to design novel peptides predicted to bind multiple types of plastic simultaneously. The AI-generated peptides showed high predicted affinity for polystyrene, polyethylene terephthalate, and polyethylene, offering a new eco-friendly approach for detecting or capturing mixed-plastic microplastic pollution.
AI-driven rational design of promiscuous and selective plastic-binding peptides
Researchers used AI-driven computational design to develop both promiscuous and selective plastic-binding peptides capable of either broadly capturing heterogeneous microplastic mixtures or specifically targeting individual polymer types, providing new tools for microplastic quantitation, capture, and degradation applications.
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.
Identification of microplastics using a convolutional neural network based on micro-Raman spectroscopy
Researchers combined micro-Raman spectroscopy with a neural network to identify microplastics, achieving over 99% accuracy across 10 different plastic types. The system was also tested on real environmental samples and performed well at classifying unknown particles. This AI-powered approach could make microplastic identification faster and more reliable for environmental monitoring.
Integrating biophysical modeling, quantum computing, and AI to discover plastic-binding peptides that combat microplastic pollution
Scientists used a combination of artificial intelligence, quantum computing, and biophysics modeling to discover new peptides (short proteins) that bind tightly to common plastics like polyethylene and polypropylene. These plastic-binding peptides could eventually be used to create biological tools for detecting, filtering, or breaking down microplastic pollution. While still in the computational stage, this approach offers a promising new path toward cleaning up microplastics in the environment.
Identifying plastics with photoluminescence spectroscopy and machine learning
Researchers showed that combining photoluminescence spectroscopy (shining light on plastic and measuring what comes back) with machine learning can reliably identify different types of plastic materials. This low-cost, widely accessible approach could help scientists track and characterize plastic pollution in the environment at a global scale.
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Researchers developed a low-cost microplastic detection system using polymer-specific peptides covalently linked to fluorescent probes, employing phage surface display technology to identify polymer-specific binding sequences. The method aims to rapidly distinguish different plastic polymer types in environmental and industrial mixed samples using fluorescent labeling combined with FTIR and Raman spectroscopy validation.
Machine Learning Method for Microplastic Identification Using a Combination of Machine Learning and Raman Spectroscopy
Researchers developed a machine learning method for identifying microplastics using a combination of multiple spectroscopic techniques, improving classification accuracy beyond single-method approaches and enabling automated polymer identification.