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61,005 resultsShowing papers similar to Optical innovations in microplastic analysis: a critical review of detection strategies
ClearOptical innovations in microplastic analysis: a critical review of detection strategies
This review surveys advances in optical methods for microplastic detection, including spectroscopic techniques, imaging systems, and sensor technologies. Researchers found that emerging approaches like surface-enhanced Raman spectroscopy combined with machine learning are enhancing automation and detection accuracy. The study identifies the need for standardized protocols and improved techniques to handle the challenges of detecting microplastics in complex environmental and biological samples.
Advanced Optical Imaging Technologies for Microplastics Identification: Progress and Challenges
This review surveys advanced optical imaging technologies used to identify microplastics and nanoplastics in environmental samples. Researchers compared techniques like Raman spectroscopy, infrared imaging, and fluorescence microscopy, noting their strengths and limitations for detecting increasingly small particles. The study highlights that improving detection methods is essential for accurately monitoring the full scope of plastic pollution.
Principles, performance and emerging trends for optical detection of environmental microplastics: A review
This review summarizes recent advances in optical detection methods for identifying microplastics in environmental samples, covering both spectroscopic techniques like Raman and infrared spectroscopy and fluorescence-based approaches using dyes such as Nile red. Researchers highlight how machine learning is improving the accuracy and efficiency of spectroscopic identification. The study also evaluates emerging fluorescent materials like carbon dots for specific microplastic identification and environmental behavior tracing.
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.
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.
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.
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.
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.
Outlook on optical identification of micro- and nanoplastics in aquatic environments
Researchers studied the optical properties of micro- and nanoplastics and evaluated near-infrared spectroscopy as a detection method for plastic particles in water, finding that optical techniques show promise for rapid, non-destructive identification. Improved optical detection methods could enable faster and more cost-effective monitoring of plastic pollution in aquatic environments.
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.
Machine learning assisted Raman spectroscopy: A viable approach for the detection of microplastics
This review covers how machine learning combined with Raman spectroscopy can improve the detection and identification of microplastics in environmental samples. Traditional detection methods are slow and have limitations in resolution and particle size analysis, but AI algorithms can process spectral data more quickly and accurately. Better detection tools are essential for understanding the true scale of microplastic contamination in our water, food, and environment.
Microplastics: Challenges, toxicity, spectroscopic and real-time detection methods
This review paper summarizes the challenges of detecting microplastics and the latest methods scientists use to find and identify them, including infrared, Raman, and fluorescence spectroscopy. It also covers the known toxic effects of microplastics on living organisms, including how they can carry harmful chemicals and pathogens. Better detection methods are important because understanding where microplastics are and what types are present is the first step toward assessing health risks.
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.
The development and application of advanced analytical methods in microplastics contamination detection: A critical review
This review evaluated advanced analytical methods for detecting microplastic contamination, covering techniques from visual identification and spectroscopy to emerging approaches, and highlighted the need for standardized, efficient methods to improve environmental monitoring.
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.
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.
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.
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.
Raman spectroscopy: Recent advances in fast and reliable microplastic analysis
This review summarized recent advances in Raman spectroscopy for fast and reliable microplastic identification, covering improvements in speed, sensitivity, and automation that are making the technique more practical for routine environmental monitoring. Raman-based methods are increasingly able to identify microplastics in complex environmental matrices including biological tissues.
Identification of microplastics using Raman spectroscopy: Latest developments and future prospects
This review summarizes the latest advances in using Raman spectroscopy to identify microplastics in environmental samples, highlighting improvements in speed, sensitivity, and the ability to characterize plastic type and surface chemistry.
Artificial intelligence-driven detection of microplastics in food: A comprehensive review of sources, health risks, detection techniques, and emerging artificial intelligence solutions
This review compares traditional and advanced methods for detecting microplastics in food, finding that while older techniques provide basic information, newer technologies like infrared spectroscopy and mass spectrometry can identify much smaller particles with greater accuracy. The study highlights how artificial intelligence can significantly improve the speed and precision of microplastic detection in food products. As global plastic production continues to rise, better detection methods are essential for monitoring food safety and protecting human health.
Recent Trends in Microplastic Detection based on Machine Learning and Artificial Intelligence
This chapter reviews recent trends in using machine learning and artificial intelligence for microplastic detection, addressing limitations of traditional microscopic and spectroscopic methods. The authors highlight how hyperspectral imaging combined with ML algorithms can classify and quantify microplastic samples more effectively, with improved recognition speed and cost-efficiency. The study suggests that AI-based approaches have significant potential for advancing large-scale microplastic monitoring.
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.
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.