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Papers
61,005 resultsShowing papers similar to Synergizing Machine Learning Algorithm with Triboelectric Nanogenerators for Advanced Self-Powered Sensing Systems
ClearMicroplastic 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.
Detection 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.
Triboelectric Nanogenerator-Based Electronic Sensor System for Food Applications
This paper is not about microplastics. It reviews triboelectric nanogenerator (TENG) technology for food safety applications, including self-powered sensors for detecting contaminants during food production and monitoring. While TENGs use polymer materials and the review mentions environmental protection broadly, the study focuses on food quality testing technology rather than microplastic contamination.
Research Progress in Fluid Energy Collection Based on Friction Nanogenerators
This review examines triboelectric nanogenerators (TENGs) as an emerging platform for harvesting fluid energy including wind and wave power, covering their fundamental operating principles and applications in distributed energy systems for the Internet of Things. The authors discuss device optimization strategies and evaluate the future prospects and challenges for scaling TENG-based fluid energy harvesting.
Smart and Sustainable Technological Framework for Microplastic Pollution Mitigation
Researchers proposed a smart technological framework for microplastic pollution mitigation that integrates IoT-based monitoring, machine learning analytics, and eco-friendly remediation technologies. The system uses low-power sensors for continuous detection of microplastic contamination and sustainable filtration mechanisms with biodegradable adsorbent materials for cleanup. The framework emphasizes modular design and renewable energy integration to support long-term deployment across diverse aquatic environments.
Machine Learning Advancements and Strategies in Microplastic and Nanoplastic Detection
This systematic review looks at how machine learning is improving our ability to detect tiny microplastics and nanoplastics in the environment. Better detection methods matter because accurately measuring plastic contamination is the first step toward understanding — and reducing — human exposure.
Self-Powered Water Quality Monitoring Using AlGaN/GaN Hemt Powered By Rotational Teng
Researchers built a self-powered water quality sensor combining an AlGaN/GaN transistor with a triboelectric nanogenerator that harvests energy from water flow, achieving nanomolar-level detection of heavy metals, pesticides, and microplastics without an external power source.
Machine LearningAdvancements and Strategies in Microplasticand Nanoplastic Detection
This systematic review summarizes how machine learning technology is being used to detect microplastics and nanoplastics in the environment. Better detection methods are important because understanding where these particles are and how much is present is the first step toward assessing risks to human health.
High Performance Triboelectric Nanogenerators from Compostable Cellulose‐Biodegradable Poly(Butylene Succinate) Composites
This paper is not directly about microplastics; it develops biodegradable triboelectric nanogenerators from poly(butylene succinate) and cellulose composites as a plastic-free alternative to conventional devices that end up in landfills, addressing the broader problem of polymer waste but not microplastic contamination specifically.
A Power Management and Control System for Environmental Monitoring Devices
Researchers developed a universal power management system for automated environmental monitoring devices used in smart agriculture. The system is designed to efficiently handle solar energy input and power distribution to sensors and processing units for continuous field operation. While not directly about microplastics, the technology could support the kind of continuous environmental monitoring needed to track pollution levels including microplastic contamination.
Recent advances in the application of machine learning methods to improve identification of the microplastics in environment
This review examined a decade of progress in applying machine learning algorithms to microplastic identification, finding that support vector machines and artificial neural networks significantly improve detection accuracy and efficiency when combined with spectroscopic techniques like FTIR and Raman.
Artificial Intelligence and Machine Learning Approaches for Automatic Microplastics Identification and Characterization
This review examines how artificial intelligence and machine learning algorithms are being applied to identify, characterize, and model microplastic pollution in the environment. The authors found that these tools can analyze large sensor datasets to detect microplastics in water bodies, predict transport patterns, and model adsorption behavior under various environmental conditions. The study highlights the growing role of computational approaches in understanding and mitigating microplastic contamination.
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.
Current applications and future impact of machine learning in emerging contaminants: A review
This review examines how machine learning is being applied to emerging contaminant research including microplastics, covering identification, environmental behavior prediction, bioeffect assessment, and removal optimization of these pollutants.
How AI methods enhance the design and performance of nanophotonic environmental sensors: a systematical review
This review examines how artificial intelligence methods including machine learning and deep learning are being integrated with nanophotonic sensor platforms to enhance environmental monitoring capabilities, with applications including microplastic and contaminant detection in portable, real-time systems.
Bridging Nanomanufacturing and Artificial Intelligence—A Comprehensive Review
This review covers how artificial intelligence and machine learning are being applied to nanomanufacturing for medicine, robotics, and electronics. While not about microplastics directly, the AI-powered nanoscale detection and characterization methods discussed could be applied to identifying and quantifying nanoplastics in the environment and human tissue. Advances in nano-scale imaging and analysis driven by AI may eventually help researchers better understand human exposure to nanoplastics.
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.
Artificial intelligence and IoT driven technologies for environmental pollution monitoring and management
This review explores how artificial intelligence and Internet of Things sensors can be used to detect and monitor environmental pollutants, including microplastics, in air, water, and soil. Machine learning methods show promise for improving pollution tracking and prediction, but challenges remain around data sharing and model reliability. Advanced monitoring technology could play a key role in identifying and managing microplastic contamination in the environment.
Self-powered portable photoelectrochemical sensor based on dual-photoelectrode for microplastics detection
Researchers developed a portable, self-powered sensor that can detect polystyrene microplastics in water at concentrations as low as 1 part per billion. The sensor works without batteries by using light energy and maintains over 97% accuracy even when other pollutants are present. Better detection tools like this could help monitor microplastic contamination in drinking water and food systems, which is a key step toward understanding and reducing human exposure.
Enhanced Area Triboelectric Nanogenerator Utilizing Recycled Single Used Plastic Bubble Wrap and Discarded Sketching Paper Ensuring Circularity of Material
Researchers developed a low-cost triboelectric nanogenerator (TENG) made from recycled single-use bubble wrap and discarded sketching paper, demonstrating a way to convert waste plastics into functional energy-harvesting devices. The study suggests that repurposing plastic waste into sustainable electronics could help address both plastic pollution and energy consumption concerns.
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.
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.
When microplastics meet electroanalysis: future analytical trends for an emerging threat
This review examines the evolution of analytical methods for detecting microplastics, highlighting the emerging advantages of electroanalytical sensors — particularly for sub-micron particles — over traditional spectroscopic and thermal methods, and discussing the growing role of artificial intelligence in automated microplastic analysis.
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.