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61,005 resultsShowing papers similar to How AI methods enhance the design and performance of nanophotonic environmental sensors: a systematical review
ClearHow 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.
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.
Recent progress and technological advancements for detection of micro/nano-plastics in the environment
This review surveys the latest analytical tools for detecting micro- and nanoplastics across environmental samples, covering imaging, spectroscopy, electrochemical sensors, and artificial intelligence. It highlights how the very small size and chemical complexity of nanoplastics makes detection especially challenging, and discusses how AI integration is improving accuracy and throughput. Advancing detection methods is foundational to understanding the true scale of microplastic contamination and its risks to ecosystems and human health.
The supporting role of Artificial Intelligence and Machine/Deep Learning in monitoring the marine environment: a bibliometric analysis
This review examines the supporting role of artificial intelligence and machine learning in monitoring and managing plastic pollution, covering applications in remote sensing, image-based plastic detection, and predictive modeling of plastic fate. The authors identify deep learning for image classification and satellite-based detection as the most rapidly advancing AI applications in plastic pollution science.
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.
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.
A Critical Review on Artificial Intelligence—Based Microplastics Imaging Technology: Recent Advances, Hot-Spots and Challenges
Researchers reviewed the use of artificial intelligence and machine learning techniques for detecting and identifying microplastics in environmental samples. The study found that AI-based imaging tools can significantly speed up analysis and improve accuracy compared to traditional manual methods. However, challenges remain around standardizing datasets and making these tools accessible for routine environmental monitoring.
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.
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.
The Role of Artificial Intelligence in Microplastic Pollution Studies and Management
This review explores how artificial intelligence is transforming microplastic research, from automating detection in microscopy images and spectral analysis to predicting how plastics interact with pollutants and living organisms. AI-powered sensors and real-time monitoring systems are also being integrated into wastewater treatment to reduce microplastic release, making the technology a powerful tool for both understanding and managing plastic pollution.
Recent advances and future technologies in nano-microplastics detection
Researchers reviewed the latest technologies for detecting microplastics and nanoplastics (tiny plastic particles found even in remote environments), including AI-driven classification and advanced microscopy techniques. As particle sizes shrink, detection becomes harder, and the lack of standardized methods remains a major barrier to understanding their full impact on ecosystems and human health.
Advancing environmental sustainability through emerging AI-based monitoring and mitigation strategies for microplastic pollution in aquatic ecosystems
This review explores how artificial intelligence technologies, including machine learning, computer vision, and remote sensing, can improve the detection, tracking, and removal of microplastic pollution in waterways. Researchers found that AI-based approaches offer significant advantages over traditional monitoring methods for identifying microplastic distribution patterns. The study highlights the potential of AI-driven robotic systems to support more efficient and scalable environmental cleanup efforts.
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.
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 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.
Artificial intelligence in microplastic detection and pollution control
This review examines how artificial intelligence is being combined with spectroscopy and imaging techniques to dramatically improve the speed and accuracy of microplastic detection in the environment. Better detection methods are essential for tracking the sources and spread of microplastic pollution that ultimately affects human health through contaminated food and water.
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.
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.
Role of AI in Microplastic Pollution Detection and management studies
This review assessed how artificial intelligence approaches—including machine learning and deep learning—are being applied to detect, identify, and monitor microplastics in environmental and biological samples. The authors found AI substantially accelerates microplastic characterization workflows but that training data quality and standardization across studies remains a limiting factor.
Transformative role of deep learning in Raman spectroscopy-based detection of microplastics and nanoplastics
This review examines how deep learning is transforming the detection and classification of micro- and nanoplastics using Raman spectroscopy. Researchers found that artificial intelligence can automate spectral analysis, enabling higher-throughput and more accurate identification of plastic particles. However, most deep learning approaches have only been validated with controlled laboratory samples, and their reliability in complex environmental samples still needs improvement.
An Artificial Intelligence based Optical Sensor for Microplastic Detection in Seawater
Researchers developed an AI-based optical sensor system combining an optical detection subsystem and an image acquisition subsystem to detect and identify microplastic particles in seawater, distinguishing them from naturally occurring marine particles. The device applies AI algorithms to analyze consecutive image frames and classify particles as microplastic or non-microplastic, with the full system housed in two portable cases.
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.
Advancing microplastic surveillance through photoacoustic imaging and deep learning techniques
Researchers developed a new method for detecting and characterizing microplastics using photoacoustic imaging combined with deep learning algorithms. The approach enables high-resolution visualization of microplastic morphology and distribution in environmental samples. The study suggests that this integrated imaging and AI technique could significantly advance environmental monitoring capabilities for tracking microplastic contamination.
Optical innovations in microplastic analysis: a critical review of detection strategies
This review examines recent advances in optical methods for detecting microplastics, including spectroscopy, imaging techniques, and emerging sensor technologies like surface-enhanced Raman spectroscopy and fluorescence lifetime imaging. Researchers found that AI-driven computational models are significantly improving the speed and accuracy of microplastic identification. However, challenges remain with organic matter interference and the lack of standardized detection protocols across laboratories.