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61,005 resultsShowing papers similar to Artificial Intelligence (AI) Based Rapid Water Testing System
ClearArtificial 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.
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-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.
Real-time detection for water pollutant based on triboelectric nanogenerators and machine learning
Scientists have developed a new device that can detect dangerous pollutants in water—including heavy metals, microplastics, and rust—by running water through a special sponge that creates electrical signals when contaminated. The system correctly identified these harmful substances 87% of the time and could work in different temperatures and water conditions. This technology could help communities quickly test their drinking water for pollutants that can cause health problems, potentially making water safety monitoring faster and more affordable.
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.
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.
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.
Detecting Chemical Contaminants in Water Using AI
This review examines how artificial intelligence and machine learning tools are being applied to detect chemical contaminants in water, including microplastics, covering sensor technologies, data processing approaches, and the potential for real-time monitoring systems.
Real-Time Detection of Microplastics Using an AI Camera
Researchers developed a camera-based system using artificial intelligence to detect and measure microplastics in real time as they move through water. The system was tested with three different camera setups and could identify particles, measure their size, and track their speed. This technology could provide a faster and more practical alternative to the labor-intensive laboratory methods currently used to monitor microplastic pollution.
Application of Laser-Induced, Deep UV Raman Spectroscopy and Artificial Intelligence in Real-Time Environmental Monitoring—Solutions and First Results
Researchers tested a deep UV Raman spectrometer combined with artificial intelligence for real-time detection of nitrates, selected pharmaceuticals, and common microplastic polymers in water. The system demonstrated feasibility for continuous environmental monitoring of aquatic systems without extensive sample preparation.
How AI methods enhance the design and performance of nanophotonic environmental sensors: a systematical review
Researchers reviewed how combining artificial intelligence with nanophotonic sensors — devices that use light at the nanoscale — dramatically improves the detection of environmental pollutants including microplastics, heavy metals, and organic chemicals. The pairing enables faster, more accurate, and portable real-time environmental monitoring.
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.
The Development of Sensors for Microplastic Detection Using Artificial Intelligence
This review examined AI-enhanced sensors developed for microplastic detection and characterization in aquatic environments, covering machine learning, deep learning, and spectroscopic sensor approaches. The authors found that AI substantially reduces the labor intensity of microplastic identification and improves detection of small particles, though training dataset standardization and real-world validation remain priority challenges.
Real-time detection of microplastics in aquatic environments using emerging technologies
Researchers proposed a real-time microplastic detection system combining AI-enhanced optical sensors and IoT devices, capable of automatically classifying microplastics in ocean water without the time-consuming manual steps required by spectroscopy or microscopy.
Machine Learning-Enhanced Raman Spectroscopy for Microfiber Detection: From Model Development to Coastal Investigation.
Scientists developed a new method using artificial intelligence to quickly identify tiny plastic fibers in ocean water, which are the most common type of microplastic pollution. The method can accurately detect these microscopic plastic pieces in just 5 minutes, compared to much longer traditional methods. This faster detection is important because microplastics are found throughout our environment and food chain, and better monitoring could help reduce our exposure to these potentially harmful particles.
Possibilities of Real Time Monitoring of Micropollutants in Wastewater Using Laser-Induced Raman & Fluorescence Spectroscopy (LIRFS) and Artificial Intelligence (AI)
Researchers developed a real-time monitoring method combining deep-UV laser-induced Raman and fluorescence spectroscopy with AI-based analysis to detect micropollutants in wastewater treatment plants across different treatment stages.
Development of an Iot-Integrated AI System for Microplastic Detection in Water Samples
Researchers developed an IoT-integrated AI system using high-resolution microscopy, a Raspberry Pi platform, and machine learning to detect and classify microplastic particles in water samples in real time via MQTT, achieving detection accuracy exceeding 95% in simulated dataset validation.
Advancements and challenges in microplastic detection and risk assessment: Integrating AI and standardized methods
This review examines current methods for detecting and measuring microplastics in water, soil, and biological samples, including microscopy and spectroscopy techniques. The authors highlight how artificial intelligence could make detection faster and more accurate. Standardized testing methods and better health risk assessments are needed to understand and manage the dangers microplastics pose to human health.
Integrating Machine Learning and IoT Technologies for Smart Water Quality Monitoring: Methods, Challenges, and Future Directions
Machine learning and IoT sensor technologies were integrated into a smart environmental monitoring system designed for real-time detection of pollutants including microplastics. The platform demonstrates how digital technologies can improve the spatial and temporal resolution of environmental contamination surveillance.
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.
Nanoplastics in Water: Artificial Intelligence-Assisted 4D Physicochemical Characterization and Rapid In Situ Detection
Researchers developed an artificial intelligence-powered holographic microscopy system that can detect and classify nanoplastics in water in real time, without any sample preparation. The technology identified particles as small as 135 nanometers and tracked their movement in three dimensions. This represents a significant advancement in environmental monitoring, as previous methods required extensive lab processing to detect plastic particles this small.
IoT-Driven Image Recognition for Microplastic Analysis in Water Systems using Convolutional Neural Networks
Researchers developed an IoT-based system using artificial intelligence to automatically detect and count microplastics in water samples through image recognition. The system uses cameras at distributed sensor points to continuously monitor waterways and can identify microplastics of different sizes, shapes, and colors. This technology could improve environmental monitoring of microplastic pollution in real time, helping communities and agencies respond faster to contamination threats in drinking water sources.
Detecting Microplastics in Seawater with a Novel Optical Sensor Based on Artificial Intelligence Models
Detecting microplastics in seawater quickly and accurately is a major technical challenge, and this study developed a novel optical sensor that uses artificial intelligence to identify plastic particles from light-scattering data in real time. The AI-powered system was tested on seawater samples and showed promising accuracy for classifying microplastic types without the need for time-consuming laboratory processing. Automated in-situ sensors like this could enable continuous, large-scale ocean monitoring for microplastic pollution.
High-throughput microplastic assessment using polarization holographic imaging
Researchers built a portable, low-cost system that uses holographic imaging and polarized light combined with deep learning to automatically detect, count, and classify microplastics in water in real time — without lengthy sample preparation. This tool significantly speeds up microplastic monitoring and could be widely deployed for environmental surveillance.