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Papers
20 resultsShowing papers similar to A Microwave-Based Sensing Platform for Microplastic Detection and Quantification: A Machine Learning-Assisted Approach
ClearA Microwave-Based Sensing Platform for Microplastic Detection and Quantification: A Machine Learning-Assisted Approach
Researchers developed a low-cost microwave spiral sensor that can detect and differentiate three common types of microplastic (PTFE, PVC, PET) in water, achieving the highest sensitivity reported for microwave-based approaches and using machine learning to identify unknown polymer types. Affordable, reliable detection tools like this are critical for routine environmental monitoring of microplastic contamination in drinking water and waterways.
Microfluidic Microwave Sensor for Rapid Detection of Microplastics in Water: Optimization, Modeling, and Performance Evaluation
Researchers developed a microfluidic sensor that uses microwave technology to rapidly detect microplastics in water samples without physical contact. The sensor was optimized to distinguish between different concentrations and sizes of plastic particles with high sensitivity. The technology could enable faster and more practical on-site monitoring of microplastic contamination in water supplies.
Detection of microplastics by microfluidic microwave sensing: An exploratory study
Researchers developed a compact microwave sensor on a microfluidic chip to detect microplastics in water samples. The system works by measuring how the presence of plastic particles changes the electrical properties of water. While the technology shows promise as a rapid and portable detection method, its current sensitivity needs improvement before it can detect the low microplastic concentrations typically found in natural freshwater.
Size and concentration characterization of microplastic particles in aqueous samples using sensitivity-enhanced coupled planar microwave resonators
Researchers developed a novel microwave sensing platform for real-time detection and characterization of microplastic particles in water samples. The sensor uses an enhanced coupled planar microwave resonator design with a low-cost disposable sample holder, enabling rapid, non-destructive measurement of microplastic particle size and concentration without cross-contamination between tests.
Microplastics Detection with Microfluidic Near-Field Microwave Sensors
A new microfluidic sensor integrating a microwave detector was developed that can identify microplastics in water in real time without labelling, by measuring how particles change the dielectric properties of the water flowing through the device. This kind of low-cost, continuous-monitoring sensor could make routine environmental surveillance for microplastic contamination more practical.
Approaches to Detect Microplastics in Water Using Electrical Impedance Measurements and Support Vector Machines
Researchers developed an electrical impedance spectroscopy method enhanced with machine learning to detect microplastics in water, achieving over 98% classification accuracy for stationary samples and over 85% for dynamic flow measurements across different plastic materials and particle sizes.
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.
Microplastic Identification Using Impedance Spectroscopy and Machine Learning Algorithms
Scientists developed a new method to detect and classify microplastics in water using electrical measurements and machine learning. The system can identify different sizes of PET microplastic particles with high accuracy, offering a potential tool for real-time water quality monitoring. Better detection methods like this are important for understanding how much microplastic contamination exists in drinking water and other water sources.
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.
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.
Portable Multichannel Measurement System for Real-Time Microplastics Assessment Using Microwave Sensors
Scientists developed a portable multichannel electronic system that uses microwave sensors to detect microplastics in water in real time, capable of simultaneously reading up to four sensors targeting particles of different sizes. The system combines radio-frequency integrated circuits with signal-conditioning hardware for field-deployable monitoring. This kind of low-cost, portable sensing technology could make routine microplastic screening much more practical at waterways and treatment facilities.
Microplastic Detection in Soil and Water Using Resonance Microwave Spectroscopy: A Feasibility Study
Researchers conducted a feasibility study using resonance microwave reflectometry to detect and quantify microplastics in soil and water, demonstrating that microplastic concentration could be expressed as a linear function of measured S11 resonance frequency shifts in artificially prepared samples.
Passive Disposable Microwave Sensor for Online Microplastic Contamination Monitoring
Researchers developed a passive disposable microwave sensor for online monitoring of microplastic contamination in water, using a sensitivity-enhanced planar dual-resonator tag-reader structure combined with a silicon resonator to enable non-contact concentration measurement.
Cost-Effective and Wireless Portable Device for Rapid and Sensitive Quantification of Micro/Nanoplastics
Researchers developed a wireless portable device for rapid quantification of micro- and nanoplastics in water samples, offering a field-deployable alternative to laboratory-based analysis for environmental monitoring.
RF MEMS Resonance Sensor for Measuring Microplastics Concentration
Researchers designed an RF MEMS resonance sensor capable of detecting microplastics in water at low cost, offering a practical alternative to expensive conventional particle analyzers for environmental monitoring.
Microwave Cytometry with Machine Learning for Shape-Resolved Microplastic Detection
Researchers developed a microwave cytometry platform paired with a random forest model trained on microscopy-derived shape data to electronically determine the major and minor axes of ellipsoidal microplastic particles with less than 8% average error, removing the spherical-particle assumption that limits existing flow-through sensors.
Microplastics detection with microfluidics integrated with a microwave sensor
Researchers developed a microwave sensor integrated with a microfluidic channel for real-time microplastics detection in water, using finite-element simulations to optimize sensitivity and validating performance experimentally. The system achieved a sensitivity of 0.1 dB and detected microplastics as small as 600 micrometers through measurement of reflected signal variations.
Sub-6 GHz Microwave Sensor Targeting Microplastic Detection
Researchers designed an enhanced sub-6 GHz microwave sensor for low-cost microplastic detection by modifying sensor geometry to better utilize the bandwidth of portable microwave vector network analyzers. Electromagnetic simulations and experimental measurements validated the redesigned sensor, which calibrates resonant frequency as a function of effective permittivity to quantify microplastic concentrations in water.
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.
Detection of microplastics in water using electrical impedance spectroscopy and support vector machines
Researchers developed an electrical impedance spectroscopy method combined with support vector machine classifiers that can distinguish polypropylene and polyolefin microplastics in water — including at varying salinity and organic content — offering a promising approach for rapid in-situ microplastic detection.