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Papers
61,005 resultsShowing papers similar to Real-time detection for water pollutant based on triboelectric nanogenerators and machine learning
ClearDetection of Microplastics Based on a Liquid–Solid Triboelectric Nanogenerator and a Deep Learning Method
Scientists developed a new microplastic detection device based on a liquid-solid friction generator combined with deep learning AI to identify different types of plastic particles. The system can classify microplastics by material type with high accuracy using electrical signals generated when plastic particles contact a liquid surface. This technology could make it easier and cheaper to monitor microplastic contamination in water supplies.
Microplastic detection and recognition system enabled by a triboelectric nanogenerator and machine learning techniques
Researchers developed a simple, rapid microplastic detection and identification device combining liquid-solid contact electrification with machine learning algorithms. The system could distinguish between different types of microplastics in water based on open-circuit voltage differences, offering a lower-cost and faster alternative to conventional detection methods.
Artificial Intelligence (AI) Based Rapid Water Testing System
Researchers developed an AI-powered portable water testing system that combines multiple sensing techniques, including capacitance, resistance, UV, infrared, and Raman spectroscopy, to detect contaminants in real time. The system can identify a wide range of pollutants including microplastics, heavy metals, and organic compounds within seconds. The device aims to provide an accessible, rapid monitoring tool for water quality assessment in both industrial and domestic settings.
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.
Artificial Intelligence (AI) Based Rapid Water Testing System
Researchers developed an AI-powered portable water testing system that combines five analytical techniques to detect contaminants including heavy metals, pathogens, and microplastics in real time. The device uses an embedded machine learning model trained on diverse water samples to recognize contamination patterns. The study demonstrates a cost-effective approach to rapid water quality monitoring that could help identify microplastic pollution in both industrial and domestic water supplies.
Artificial intelligence (AI) based rapid water testing system
Researchers developed an AI-powered portable water testing system that integrates five analytical techniques for real-time water quality monitoring. The system can detect a range of contaminants including microplastics, heavy metals, and pathogens within seconds, offering a cost-effective alternative to traditional laboratory-based water testing for both industrial and domestic use.
An Edge-Deployable Multi-Modal Nano-Sensor Array Coupled with Deep Learning for Real-Time, Multi-Pollutant Water Quality Monitoring
Scientists developed a compact multi-sensor array that combines three different nanotechnology-based detection methods with deep learning to monitor water pollutants in real time, including nanoplastics at very low concentrations. The device achieved detection limits as low as 87 nanograms per liter for nanoplastics and can process data in just 31 milliseconds on low-power hardware. Field tests in municipal water systems showed the sensor maintained high accuracy even in complex real-world conditions.
Smart sensor networks for tracking the evolution of water pollution hotspots and hot moments through river networks
Scientists developed a new smart sensor system that can detect when and where dangerous pollution spikes occur in rivers and streams. These pollution "hotspots" and sudden contamination events can include harmful substances like microplastics, metals, and nutrients that threaten drinking water safety. The technology helps communities better predict and respond to water contamination before it reaches people downstream.
Innovative prototype for the mitigation of water pollution from microplastics to safeguard the environment and health
Researchers developed an innovative prototype device for removing microplastics from water through a combination of filtration and electrocoagulation, demonstrating high MP removal efficiency from both synthetic and real water samples in controlled trials.
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.
Microplastic Detection in Water Using a Sensor Network, An Electronic Tongue and Spectroscopy Impedance
Researchers developed an electronic sensor system using impedance spectroscopy to detect microplastics in drinking water without needing expensive laboratory equipment. By running 160 experiments with different water contaminant combinations, they showed that the technique can distinguish microplastic contamination using electrochemical signals and statistical analysis. Affordable, portable detection systems like this are important for monitoring water supplies in regions where lab infrastructure is limited.
Machine learning-driven optical microfiltration device for improved nanoplastic sampling and detection in water systems
Researchers developed a new device combining agarose-based microfiltration with machine learning-assisted Raman spectroscopy to detect nanoplastics in water more accurately and efficiently. The system achieved over 96% accuracy in identifying nanoplastic particles while dramatically reducing analysis time compared to traditional methods. Better detection tools like this are essential for monitoring nanoplastic levels in drinking water and assessing risks to human health.
Clasificación de microplásticos usando lenguas electrónicas
Colombian engineers developed an electrochemical sensor array (electronic tongue) combined with pattern recognition algorithms to classify microplastics in drinking water samples in near-real time. The system successfully distinguished between clean tap water and water spiked with PET microplastics, demonstrating a potential alternative to slow, labor-intensive laboratory methods. Fast, continuous monitoring tools like this are critical for water utilities that need early warning of 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.
Eco-Sensing System for Water Pollution and Microplastic Detection
This study evaluates new sensor-based and spectroscopic technologies for detecting microplastics in water in real time, comparing them with traditional lab-based methods. The portable systems showed improved accuracy and efficiency for field use, making it possible to monitor microplastic contamination as it happens. Better detection tools are essential for protecting drinking water sources and understanding the true scale of human microplastic exposure.
Artificial Intelligence-Based Microfluidic Platform for Detecting Contaminants in Water: A Review
This review explores how microfluidic devices combined with artificial intelligence can detect water pollutants including microplastics and nanoplastics in real-time, outside the laboratory. Traditional water testing requires large lab equipment, but these portable chip-based systems can identify contaminants quickly and accurately using machine learning. This technology could improve monitoring of microplastic contamination in drinking water and other water sources.
Measuring Microplastic Concentrations in Water by Electrical Impedance Spectroscopy
Researchers developed a method using electrical impedance spectroscopy to measure microplastic concentrations in water samples without requiring complex laboratory equipment. The technique can distinguish between different concentrations and types of plastic particles based on their electrical properties. The study offers a potentially faster and more accessible approach for routine microplastic monitoring in water treatment and environmental settings.
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.
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.
Efficient Data-Driven Machine Learning Models for Water Quality Prediction
This study tested machine learning methods for predicting water quality based on physical, chemical, and biological measurements. While focused on water safety testing rather than microplastics specifically, the automated classification tools developed here could help water treatment facilities quickly identify contaminated water. Better monitoring technology is important because current methods for detecting microplastics in water are slow and expensive.
Harnessing Deep Learning for Real-Time Water Quality Assessment: A Sustainable Solution
Researchers developed a deep learning system that can predict water quality in real time based on measurements like pH, turbidity, and dissolved solids. While not directly about microplastics, this kind of AI-powered monitoring tool could eventually be adapted to detect microplastic contamination in water supplies more quickly and affordably than current lab-based methods.
Cloud-Based Smart Water Quality Monitoring System using IoT Sensors and Machine Learning
Researchers developed a cloud-based smart water quality monitoring system using IoT sensors and machine learning to detect contamination parameters such as pH, nitrate, conductivity, and fecal coliform in real time. The system applies machine learning classification to correlated sensor data to enable early detection of health hazards from contaminated water sources.
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.
Nano-Engineering for Clean Water Solutions
Scientists have reviewed how tiny engineered particles (nanotechnology) can help clean water by removing dangerous pollutants like heavy metals, leftover medicines, and microplastics that traditional filters often miss. These nano-scale materials work better than current methods because they can target specific contaminants and use less energy. While this technology shows great promise for providing safer drinking water worldwide, researchers still need to study whether these tiny particles themselves might be harmful to people or the environment.