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61,005 resultsShowing papers similar to Recent progress and technological advancements for detection of micro/nano-plastics in the environment
ClearRecent 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.
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.
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.
Emerging analytical frontiers in microplastic detection: From spectroscopy to smart sensor technologies
Researchers reviewed the latest tools for detecting microplastics and nanoplastics, covering methods from laser-based spectroscopy and heat-based chemical identification to electrochemical sensors and AI-powered analysis. The review highlights that while no single method can do everything, combining these approaches — especially with machine learning — is moving the field toward faster, cheaper, and more accurate detection in water, food, and human tissue.
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.
Challenges and Recent Analytical Advances in Micro/Nanoplastic Detection
This review covers the challenges scientists face in detecting and measuring micro- and nanoplastics in the environment, especially for particles smaller than one micrometer. Current analytical methods have significant limitations for identifying nanoplastics due to their extremely small size and diverse chemical compositions. Improving detection technology is essential for accurately assessing how much microplastic contamination exists in water, food, and human tissues.
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.
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.
Using artificial intelligence to rapidly identify microplastics pollution and predict microplastics environmental behaviors
This review summarizes how artificial intelligence and machine learning are being used to identify, track, and predict the environmental behavior of microplastics in soil and water. AI methods can analyze the chemical composition, shape, and distribution of microplastics faster and more accurately than traditional techniques. The technology could help scientists better understand where microplastics accumulate and what risks they pose to ecosystems and human health.
Advancing Micro Plastic Analysis: A Comprehensive Review of Detection and Characterization Techniques
This review evaluates the current state of microplastic detection and characterization methods, from visual identification to advanced spectroscopic techniques. Researchers highlighted the challenges of detecting microplastics in complex environmental samples and the limitations of existing approaches. The study calls for standardized methods and emerging technologies to improve the accuracy and reliability of microplastic analysis across different ecosystems.
Chemical Analysis of Microplastics and Nanoplastics: Challenges, Advanced Methods, and Perspectives
This review covers the latest laboratory methods for detecting and measuring microplastics and nanoplastics in environmental samples like water, food, and air. Identifying these tiny particles is extremely challenging because they vary enormously in size, shape, and plastic type, and concentrations can differ by billions of times between samples. Better standardized detection methods are essential for accurately understanding how much microplastic humans are actually exposed to.
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.
Challenges and Advances in Analytical Techniques to Detect Micro- and Nanoplastics
This research review summarizes the current methods scientists use to detect and study microplastics and nanoplastics - tiny plastic particles that can get into our environment, food, and bodies. The authors explain that identifying these extremely small plastic pieces is very challenging and requires advanced laboratory techniques to understand what types of plastics they are and how much is present. Better detection methods are important because we need to understand how much plastic pollution we're exposed to and its potential effects on human health.
Advances in machine learning for the detection and characterization of microplastics in the environment
This review examines how machine learning and artificial intelligence are being used to speed up and improve the detection of microplastics in the environment. Techniques like neural networks and computer vision can now automatically identify plastic types and count particles much faster than traditional manual methods, though challenges remain in standardizing these approaches.
Artificial intelligence in microplastics domain: Current progress, challenges, and sustainable prospects
This critical review assesses how artificial intelligence tools—including machine learning and image recognition—are being applied to detect, characterize, and predict the behavior of microplastics in the environment. AI approaches show promise for overcoming persistent bottlenecks in large-scale microplastic analysis, but the authors highlight challenges around data quality, model interpretability, and standardization that must be addressed for these tools to reach their potential.
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.
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.
Analytical Techniques for the Detection and Characterization of Microplastics: an Overview
This overview reviews state-of-the-art analytical methods for identifying and characterizing microplastics, covering spectroscopic and microscopic approaches and their strengths and limitations for detecting plastic particles across environmental compartments including water, soil, and biological samples.
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.
Electrochemical approaches for detecting micro and nano-plastics in different environmental matrices
This review evaluates electrochemical sensor technologies as alternatives to conventional spectroscopy methods for detecting micro- and nanoplastics in environmental samples. Researchers found that electrochemical approaches offer advantages in cost, portability, and speed, making them better suited for widespread field monitoring. The study identifies key technical challenges that need to be resolved before these sensors can be broadly adopted for routine environmental surveillance.
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.
Recent advances on the methods developed for the identification and detection of emerging contaminant microplastics: a review
This review surveyed recent advances in methods for detecting and identifying microplastics in environmental samples. Researchers compared techniques including spectroscopy, microscopy, and newer automated approaches, evaluating their strengths and limitations. The study highlights that while detection capabilities have improved significantly, there is still a need for standardized, cost-effective methods that can reliably identify very small microplastic particles.
Spectro‐Microscopic Techniques for Studying Nanoplastics in the Environment and in Organisms
This review examined spectro-microscopic techniques available for detecting and studying nanoplastics in environmental and biological samples. The study highlights that detecting nanoplastics remains challenging because their small size falls below the detection limits of common analytical tools, and their chemical composition is similar to organic matrices, making identification difficult.
New Analytical Approaches for Effective Quantification and Identification of Nanoplastics in Environmental Samples
This review assessed new analytical approaches for quantifying and identifying nanoplastics in environmental samples, highlighting fundamental challenges in detection due to their small size and the need for improved methods to understand nanoplastic contamination levels.