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Papers
61,005 resultsShowing papers similar to Detection and classification of microplastics in marine environment using a low-cost, compact, and fast sensor
ClearFeasibility Study for the Development of a Low-Cost, Compact, and Fast Sensor for the Detection and Classification of Microplastics in the Marine Environment
A feasibility study demonstrated that a compact, low-cost sensor using just three infrared photodiodes can classify the most common floating marine microplastics — polyethylene and polypropylene — with about 90% accuracy, making it potentially deployable on ocean drifters for large-scale monitoring. Affordable, scalable detection tools like this are critical for filling major data gaps in global microplastic distribution mapping.
Compact low-cost sensor for microplastics detection and classification in marine and aquatic environments
Researchers developed a compact, low-cost sensor for detecting and classifying microplastics in marine and aquatic environments, designed to reduce the economic burden of MP monitoring along coastlines and enable more frequent and scalable environmental surveillance.
Compact low-cost sensor for microplastics detection and classification in marine and aquatic environments
Researchers developed a compact, low-cost sensor for detecting and classifying microplastics in marine and aquatic environments, designed to reduce the economic burden of MP monitoring along coastlines and enable more frequent and scalable environmental surveillance.
Study on marine microplastics monitoring based on infrared spectroscopy technology
Researchers developed an infrared spectroscopy-based monitoring system for marine microplastics, applying support vector machine algorithms to hyperspectral images to identify plastic types and abundances in seawater. The study found microplastic abundances ranging from roughly 5 to 39 particles per litre across sampling sites, with fibers (53-68%) and debris (23-34%) as dominant shapes, demonstrating the method's feasibility for rapid environmental monitoring.
Polymer Type Identification of Marine Plastic Litter Using a Miniature Near-Infrared Spectrometer (MicroNIR)
Researchers tested a miniature near-infrared spectrometer (MicroNIR) for rapidly identifying polymer types in marine plastic litter collected from beaches, finding it could accurately distinguish common plastics like polyethylene and polypropylene. Low-cost, portable identification tools are important for large-scale monitoring of marine plastic pollution.
Development of a Near-Infrared Imaging System for Identifying Microplastics in Water
Researchers developed a near-infrared imaging system capable of automatically identifying and characterizing microplastics suspended in water, successfully obtaining material identification images without the manual sorting typically required by conventional methods.
Designing a Low-Cost Microcontroller-Based Rover for Microplastic Detection Using Deep-Learning Image Detection and Near-Infrared Spectroscopy
Researchers designed a low-cost microcontroller-based rover for detecting nurdle microplastics in shoreline environments, integrating a compressed deep-learning object detection model trained on 150 images of polyethylene pellets with an AS7263 near-infrared sensor for spectral confirmation of polyethylene. The Raspberry Pi 3-based system demonstrated efficient microplastic identification across varying lighting conditions and burial depths in sand.
Outlook on optical identification of micro- and nanoplastics in aquatic environments
Researchers studied the optical properties of micro- and nanoplastics and evaluated near-infrared spectroscopy as a detection method for plastic particles in water, finding that optical techniques show promise for rapid, non-destructive identification. Improved optical detection methods could enable faster and more cost-effective monitoring of plastic pollution in aquatic environments.
Rapid shipboard measurement of net-collected marine microplastic polymer types using near-infrared hyperspectral imaging
Researchers developed a rapid near-infrared hyperspectral imaging method for identifying polymer types in ship-collected marine microplastic samples, achieving results in minutes compared to hours for conventional methods and enabling higher-throughput ocean monitoring.
Quantification of ternary microplastic mixtures through an ultra-compact near-infrared spectrometer coupled with chemometric tools
Researchers developed a miniaturized near-infrared spectrometer paired with chemometric analysis to quantify mixtures of the three most common environmental microplastics — polypropylene, polyethylene, and polystyrene — demonstrating its promise as a portable, field-deployable detection tool.
Microplastic identification in marine environments: A low-cost and effective approach based on transmitted light measurements
Researchers designed a low-cost microplastic detection system using a standard LCD panel and a digital USB microscope to measure transmitted light through seawater samples. The compact system demonstrated effective detection and quantification of microplastics without the need for expensive laboratory instrumentation.
An Artificial Intelligence based Optical Sensor for Microplastic Detection in Seawater
Researchers developed an AI-based optical sensor system combining an optical detection subsystem and an image acquisition subsystem to detect and identify microplastic particles in seawater, distinguishing them from naturally occurring marine particles. The device applies AI algorithms to analyze consecutive image frames and classify particles as microplastic or non-microplastic, with the full system housed in two portable cases.
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.
Quantifying Marine Plastic Debris in a Beach Environment Using Spectral Analysis
Researchers analyzed shortwave infrared reflectance spectra of weathered marine plastic debris on sandy beaches, finding that polymer type significantly influences detection capability at sub-pixel surface covers relevant to remote sensing applications.
Spatial distribution of microplastics in the tropical Indian Ocean based on laser direct infrared imaging and microwave-assisted matrix digestion
Researchers characterized microplastic distribution across the tropical Indian Ocean using a new quantum cascade laser imaging method, finding an average concentration of 50 particles per cubic meter at depths of 6 meters. The new analytical approach analyzed up to 1,000 particles per hour with over 97% identification accuracy, enabling faster and more reliable monitoring.
Indoor spectroradiometric characterization of plastic litters commonly polluting the Mediterranean Sea: toward the application of multispectral imagery
Researchers used a laboratory spectrometer to measure the light reflectance of common plastic types found in the Mediterranean Sea as a step toward developing remote sensing methods to detect marine plastic pollution from satellites or aircraft. Aerial monitoring of plastic pollution could revolutionize our ability to track and manage large-scale ocean plastic contamination.
Near-Infrared Light and OpenCV as Components for Low-Cost Airborne Microplastic Detection Machine
Researchers built a low-cost airborne microplastic detection machine using near-infrared light and OpenCV image processing, successfully differentiating polyethylene, polystyrene, and polyester particles smaller than 5 mm in testing at 1–5 minute intervals.
Quantification and characterization of microplastics in surface water samples from the Northeast Atlantic Ocean using laser direct infrared imaging
Researchers quantified microplastics in Northeast Atlantic Ocean surface waters using laser direct infrared imaging, detecting particles down to 20 micrometers and revealing microplastic concentrations and polymer compositions across eight sampling locations.
Training and evaluating machine learning algorithms for ocean microplastics classification through vibrational spectroscopy
Researchers evaluated multiple machine learning algorithms for automatically classifying ocean microplastics using infrared spectroscopy data across 13 polymer types. The study found that Support Vector Machine classifiers provided the best balance of simplicity and accuracy, offering a practical tool for faster and more reliable identification of microplastic contaminants.
In-situ Microplastic Detection Sensor based on Cascaded Microring Resonators
Researchers proposed an in-situ microplastic detection sensor using cascaded germanium-on-silicon microring resonators arranged to achieve the Vernier effect, enabling high-sensitivity analysis in near and mid-infrared spectral regions. The compact sensor design aims to replace bulky laboratory equipment for field detection of marine microplastics.
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.
Degradation degree analysis of environmental microplastics by micro FT-IR imaging technology
Researchers used micro-FTIR spectral-image fusion to classify the degradation degree of polyethylene microplastics collected from coastal environments, achieving 97.1% classification accuracy and enabling estimation of environmental persistence time from spectral data.
Weathering-independent differentiation of microplastic polymers by reflectance IR spectrometry and pattern recognition
Researchers developed a weathering-independent method for identifying microplastic polymer types using reflectance infrared spectrometry combined with pattern recognition techniques including principal components analysis and classification trees, demonstrating reliable polymer differentiation even when field samples are weathered or biofouled.
Focal plane array detector-based micro-Fourier-transform infrared imaging for the analysis of microplastics in environmental samples
Researchers developed an automated protocol using focal plane array FT-IR imaging to identify and count microplastics on filters without manual sorting, dramatically increasing throughput compared to manual methods. The approach represents an important step toward standardized, high-throughput microplastic monitoring in aquatic environments.