We can't find the internet
Attempting to reconnect
Something went wrong!
Hang in there while we get back on track
Papers
61,005 resultsShowing papers similar to Feasibility Study for the Development of a Low-Cost, Compact, and Fast Sensor for the Detection and Classification of Microplastics in the Marine Environment
ClearDetection and classification of microplastics in marine environment using a low-cost, compact, and fast sensor
Engineers developed a low-cost, compact sensor using three infrared photodiodes that can identify the most common floating marine microplastics with about 90% accuracy. The sensor is designed to be mounted on ocean floats for large-scale marine 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.
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.
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.
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.
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.
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.
A Hybrid MIR-spectrum Processing Algorithm for Microplastics Analysis
Researchers developed a hybrid algorithm for classifying microplastics using their mid-infrared spectral signatures, targeting polypropylene, polyethylene, and polystyrene. The model combines principal component analysis with machine learning techniques to improve classification accuracy. The study offers an automated approach that could make routine microplastic identification faster and more reliable for environmental monitoring.
On the Potential for Optical Detection of Microplastics in the Ocean
This study examines the potential for optical methods to detect microplastics in ocean water at large spatial scales, noting that while optical detection is promising for overcoming the limitations of discrete water sampling, methods remain in early development and reference libraries of microplastic optical properties are sparse.
From macro to micro: Comprehensive marine beach litter analysis using portable NIR
Researchers conducted a comprehensive analysis of marine beach litter using portable near-infrared (NIR) spectroscopy, combining macro-litter surveys with microplastic characterisation to assess polymer composition and pollution levels. The study demonstrated that portable NIR technology can bridge the gap between macro- and micro-scale beach litter monitoring, offering a practical tool for national marine litter surveillance programmes.
Handheld portable FTIR spectroscopy for the triage of micro and meso sized plastics in the marine environment incorporating an accelerated weathering study and an aging estimation
Researchers tested a handheld portable FTIR spectrometer for rapidly identifying micro and mesosized plastic debris on beaches and in the marine environment. Portable FTIR devices enable fast field identification of plastic polymer types, making marine litter surveys more efficient.
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.
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.
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.
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.
Developing and testing a workflow to identify microplastics using near infrared hyperspectral imaging
Researchers developed a near-infrared hyperspectral imaging workflow with an open spectral database to rapidly identify microplastics by polymer type, achieving over 88% accuracy for polypropylene, polyethylene, PET, and polystyrene particles larger than 500 micrometers.
Towards a low-cost, rapid microplastic optical detection system using fluorescent staining through Nile Red for in situ ocean deployment
This study presents a proof-of-concept for a portable, low-cost microplastic detection device that uses fluorescent dye (Nile Red) and a simple optical sensor to detect plastic particles in water. The system produced a signal that scaled linearly with microplastic concentration in lab tests. Development of cheap, field-deployable sensors like this could dramatically improve our ability to monitor microplastic pollution in real time across oceans and waterways, where current lab-based methods are too expensive and slow for widespread use.
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.
Development of a rapid detection protocol for microplastics using reflectance-FTIR spectroscopic imaging and multivariate classification
Reflectance-FTIR spectroscopy was evaluated as a faster and more automated detection method for microplastics in environmental samples, with results showing strong potential for high-throughput screening. The method could reduce the time and cost of routine microplastic monitoring programs.
Intelligent Visible-Near Infrared Micro-Hyperspectral Sensing System for Rapid Chemical Mapping of Microplastics and Metal Oxides
Identifying and mapping microplastics quickly and accurately is a major challenge for environmental monitoring, and this study introduces a low-cost imaging system combining visible and near-infrared light with deep-learning AI to classify different types of microplastics and other materials. The system achieved 97% accuracy in distinguishing between eight different chemical species — including spectrally similar plastics — while being far faster and cheaper than conventional methods like electron microscopy. This technology could make large-scale microplastic screening in food, water, and environmental samples much more practical.
From Macro to Micro: Comprehensive coastal litter analysis using portable NIR
Researchers applied portable near-infrared (NIR) spectroscopy to conduct comprehensive coastal litter analysis spanning both macro- and micro-size fractions, aiming to bridge the information gap between existing monitoring strategies that separately categorize macroplastics and microplastics on beaches.
The applicability of reflectance micro-Fourier-transform infrared spectroscopy for the detection of synthetic microplastics in marine sediments
Researchers developed and validated an optimized micro-FT-IR spectroscopy protocol for detecting microplastics in coastal marine sediments, providing a detailed operating procedure. The standardized method improves detection reliability and enables comparison of results across laboratories studying sediment microplastic contamination.
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.