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Papers
61,005 resultsShowing papers similar to Novel simple accurate detection of microplastics based on image of photoluminescent nanoparticle carbon dots via machine learning and deep feature embedding
ClearIdentifying 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.
Efficient Microplastic Detection in Water Using ResNet50 and Fluorescence Imaging
Researchers applied a ResNet50 deep learning model to fluorescence microscopy images of water samples, achieving high-accuracy classification of microplastics, demonstrating that deep learning can efficiently automate microplastic identification from microscopy data.
Detection of Microplastics Using Machine Learning
Researchers reviewed and demonstrated machine learning approaches for detecting and classifying microplastics in environmental samples, finding that automated image analysis and spectral classification methods can improve the speed and accuracy of microplastic monitoring compared to manual methods.
A Deep Learning Approach for Microplastic Segmentation in Microscopic Images
Researchers developed a deep learning model for automated segmentation and classification of microplastics in microscopic images, identifying five distinct categories including fibers, fragments, spheres, foam, and film. The model achieved high accuracy while maintaining low computational requirements, making it suitable for high-throughput deployment in environmental monitoring. The study offers a tool that could help overcome the measurement bottleneck in microplastic characterization for toxicological and risk assessment studies.
Rapid Detection of Micro/Nanoplastics Via Integration of Luminescent Metal Phenolic Networks Labeling and Quantitative Fluorescence Imaging in A Portable Device
Researchers developed a portable wireless device for rapid on-site detection of micro- and nanoplastics using fluorescent labeling and machine learning-powered image analysis. The study demonstrates that this approach enables sensitive and quantitative identification of plastic particles in environmental samples, addressing the need for field-deployable monitoring tools.
Development of Microplastics Detector and Quantifier Utilizing Deep Learning Based Algorithm
Researchers developed a microplastics detector and quantifier using deep learning-based image analysis, training a neural network to identify and count microplastic particles in microscopic images. The system achieved high accuracy and offers a faster, more objective alternative to manual counting.
Innovative methods for microplastic characterization and detection: Deep learning supported by photoacoustic imaging and automated pre-processing data
Researchers developed an innovative method combining photoacoustic imaging with deep learning to rapidly detect and characterize microplastics. The photoacoustic technology captured high-resolution images of diverse microplastic samples, while the neural network automated the classification process. The study demonstrates that this combined approach could enable faster, more accurate microplastic monitoring compared to conventional methods.
Deep Learning-Based Image Recognition System for Automated Microplastic Detection and Water Pollution Monitoring
This study developed a deep learning image recognition system to automate the detection and classification of microplastics from microscopy images of water samples. The system achieved high accuracy across particle types and sizes, offering a scalable and less labor-intensive alternative to manual microscopy for large-scale water pollution monitoring.
Deep learning-powered efficient characterization and quantification of microplastics
Researchers developed an artificial intelligence framework that uses deep learning to automatically identify and quantify microplastics from infrared spectra and visual images. The system achieved high accuracy in classifying plastic types and counting particles, dramatically reducing the time needed compared to manual analysis. This tool could make large-scale microplastic monitoring faster and more consistent across different research laboratories.
Microplastic Binary Segmentation with Resolution Fusion and Large Convolution Kernels
Researchers developed an improved machine-learning model to automatically detect and segment microplastic particles in images, achieving better accuracy than previous approaches by combining multi-resolution image analysis with large convolution kernels. Reliable automated detection tools are essential for scaling up microplastic monitoring, since manual identification is too slow and inconsistent for the volumes of environmental samples that need to be processed.
Rapid Classification of Microplastics by Using the Application of a Convolutional Neural Network
Researchers used convolutional neural networks (deep learning) to automatically classify microplastic particles in microscopy images into four categories: fragments, pellets, films, and fibers. The models achieved high classification accuracy, reducing the time and labor needed for manual identification. Automated AI classification could greatly accelerate large-scale microplastic monitoring programs.
Computer vision segmentation model—deep learning for categorizing microplastic debris
Researchers developed a deep learning computer vision model for automatically categorizing beached microplastic debris from images. The segmentation model was trained to identify and classify different types of microplastic particles, reducing the need for time-consuming manual counting and laboratory analysis. The study suggests that automated image-based detection could enable more scalable and consistent monitoring of microplastic pollution along coastlines.
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.
Fluorescence Machine Vision-Based Rapid Quantitative Characterization of Microplastics
Scientists developed a new system that uses special fluorescent dye and artificial intelligence to quickly detect and count microplastics (tiny plastic particles) in samples. The technology is faster and cheaper than current methods, which could help researchers better track these particles that may pose health risks when they get into our food and water. This advance could lead to better monitoring of microplastic pollution and help protect human health.
Size- and Concentration-Resolved Detection of PET Microplastics in Real Water via Excitation–Emission Matrix Fluorescence Quenching of Polyamide-Derived Carbon Quantum Dots
Scientists developed a new method to detect tiny plastic particles (called microplastics) in drinking water using special fluorescent dots that dim when they encounter plastic pollution. The technique works best at finding very small plastic pieces—smaller than the width of a human hair—which are hardest to detect but potentially most dangerous since they can get into our bodies more easily. This could help monitor plastic contamination in tap water and other water sources we use daily, giving us better information about our exposure to these harmful particles.
Principles, performance and emerging trends for optical detection of environmental microplastics: A review
This review summarizes recent advances in optical detection methods for identifying microplastics in environmental samples, covering both spectroscopic techniques like Raman and infrared spectroscopy and fluorescence-based approaches using dyes such as Nile red. Researchers highlight how machine learning is improving the accuracy and efficiency of spectroscopic identification. The study also evaluates emerging fluorescent materials like carbon dots for specific microplastic identification and environmental behavior tracing.
Automatic Detection of Microplastics in the Aqueous Environment
Researchers developed a deep-learning system for real-time detection and counting of microplastics in freshwater, achieving high accuracy for particles 1 mm and larger.
Automatic quantification and classification of microplastics in scanning electron micrographs via deep learning
Researchers developed a deep learning system that can automatically detect and classify microplastics in scanning electron microscope images, replacing the time-consuming process of manual analysis. The system achieved high accuracy in identifying different types and shapes of microplastic particles, even very small ones that are difficult to spot by eye. This automated approach could significantly speed up microplastic monitoring and pollution assessment efforts.
Proceeding the categorization of microplastics through deep learning-based image segmentation
Researchers developed a deep learning-based image segmentation method using Mask R-CNN to automatically identify and classify microplastic shapes in microscopic images, demonstrating a practical step toward standardized and automated microplastic categorization.
Automatic Identification and Classification of Marine Microplastic Pollution Based on Deep Learning and Spectral Imaging Technology
Researchers developed an AI system combining deep learning with multispectral imaging to automatically identify and classify marine microplastics, using a feature-selection method called ReliefF to reduce noise in complex ocean samples. The approach achieved high accuracy and offers a scalable solution for large-scale ocean microplastic monitoring that outperforms traditional manual inspection.
Rapid identification of marine microplastics by laser-induced fluorescence technique based on PCA combined with SVM and KNN algorithm
Researchers developed a laser-based fluorescence method combined with machine learning algorithms to rapidly identify different types of marine microplastics. The system achieved classification accuracy above 97 percent for four common plastic types at various concentrations. The technique offers a fast, non-destructive alternative to traditional laboratory methods for monitoring microplastic pollution in ocean environments.
Classification of (micro)plastics using cathodoluminescence and machine learning
Researchers combined scanning electron microscopy with cathodoluminescence spectroscopy and machine learning to classify six common plastic types including HDPE, LDPE, PP, PA, PS, and PET at the nanoscale. Each plastic type produced a unique cathodoluminescence signature enabling classification of micro- and nanoplastics too small for conventional infrared or Raman spectroscopy.
Revolutionizing microplastic detection in water through quantum dot fluorescence
Researchers developed a novel approach using carbon quantum dots to stain microplastics, enabling fluorescence-based detection in water at low cost and with simple synthesis, demonstrating high sensitivity and selectivity without the toxicity concerns of conventional fluorescent dyes.
Advances in machine learning for the detection and characterization of microplastics in the environment
This review examines how machine learning and artificial intelligence are being used to speed up and improve the detection of microplastics in the environment. Techniques like neural networks and computer vision can now automatically identify plastic types and count particles much faster than traditional manual methods, though challenges remain in standardizing these approaches.