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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 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.
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.
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.
Trapping tiny pollutants: SERS-driven strategies for microplastics and nanoplastics detection
This review explores how surface-enhanced Raman spectroscopy (SERS) is being developed as a highly sensitive tool for detecting and identifying micro- and nanoplastics in environmental and biological samples. Researchers highlight recent advances in sensor design, the integration of machine learning for improved accuracy, and the technique's potential for real-world monitoring. The study also identifies key challenges, including signal variability and the lack of standardized methods, that need to be resolved for broader adoption.
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.
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.
Applications of Raman spectroscopy for microplastic detection and characterization: a comprehensive spectral reference
This review evaluates Raman spectroscopy as a tool for detecting and identifying microplastics across water, soil, air, and biological samples. The study consolidates reference spectra for common plastic polymers and discusses recent innovations like surface-enhanced Raman techniques that improve detection sensitivity, while also addressing challenges like fluorescence interference in complex samples.
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.
A review of recent progress in the application of Raman spectroscopy and SERS detection of microplastics and derivatives
This review covers advances in using Raman spectroscopy and surface-enhanced Raman spectroscopy (SERS) to detect and identify microplastics in the environment. These techniques offer high resolution and sensitive detection that can identify specific plastic types even at very small sizes. Better detection methods are essential for understanding the true extent of microplastic contamination and its potential risks to human health.
Machine Learning Method for Microplastic Identification Using a Combination of Machine Learning and Raman Spectroscopy
Researchers developed a machine learning method for identifying microplastics using a combination of multiple spectroscopic techniques, improving classification accuracy beyond single-method approaches and enabling automated polymer identification.
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.
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.
Towards the Development of Portable and In Situ Optical Devices for Detection of Micro-and Nanoplastics in Water: A Review on the Current Status
This review surveys the development of portable and in-situ optical devices for detecting micro- and nanoplastics in water, as most current detection methods are laboratory-based. Researchers evaluated emerging technologies including portable Raman and infrared spectroscopy, fluorescence-based sensors, and smartphone-integrated detection systems. The study identifies key technical challenges that must be overcome to enable real-time, field-based monitoring of plastic pollution in water.
Latest Advances and Developments to Detection of Micro‐ and Nanoplastics Using Surface‐Enhanced Raman Spectroscopy
This review examines the latest developments in using surface-enhanced Raman spectroscopy (SERS) to detect micro- and nanoplastics in various environmental samples. Researchers found that SERS offers significantly improved sensitivity compared to conventional methods, enabling detection of smaller plastic particles. The study suggests that SERS-based approaches hold promise for advancing nanoplastic detection, though challenges around standardization and reproducibility remain.
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.
Surface-Enhanced Raman Spectroscopy Facilitates the Detection of Microplastics <1 μm in the Environment
Researchers developed a method using surface-enhanced Raman spectroscopy to detect and identify individual microplastic particles smaller than one micrometer. This technique addresses a major gap in environmental monitoring, since most current methods cannot reliably detect the smallest microplastics that may pose the greatest risk due to their ability to enter cells and tissues.
Characterization and identification of microplastics using Raman spectroscopy coupled with multivariate analysis
Researchers developed a new method using Raman spectroscopy combined with machine learning to identify and classify seven types of microplastics with over 98% accuracy for most polymer types. The approach was also able to correctly identify real-world microplastic samples from snack boxes, water bottles, juice bottles, and medicine vials. This technique could make microplastic detection faster and more reliable compared to manual analysis methods.
Raman Microspectroscopy: Improvement in Signal Generation and Collection to Facilitate Raman Spectroscopy
Advances in Raman microspectroscopy were reviewed for improving signal generation and analysis in microplastic identification, including new detector designs and data processing algorithms. Enhanced Raman spectroscopy capabilities enable faster and more accurate polymer identification at smaller particle sizes.
Advanced microplastic monitoring using Raman spectroscopy with a combination of nanostructure-based substrates
Researchers reviewed advances in Raman spectroscopy and surface-enhanced Raman scattering (SERS) — a technique that amplifies light signals using metallic nanostructures — for detecting micro- and nanoplastics at trace concentrations in environmental samples, highlighting new plasmonic materials, 3D substrates, and microfluidic chip platforms that enable on-site monitoring.
Detection of microplastics based on spatial heterodyne Raman spectroscopy
Researchers developed a spatial heterodyne Raman spectroscopy method for detecting microplastics, offering advantages over existing techniques by reducing detection time, lowering false detection rates, and using more affordable equipment.
Raman Spectroscopy and Machine Learning for Microplastics Identification and Classification in Water Environments
Researchers combined Raman spectroscopy with machine learning algorithms for automated identification and classification of microplastics in water environments, achieving high accuracy in distinguishing different polymer types based on spectral fingerprints.
High-resolution characterization technology for micro-/nano-plastics
This review provides an overview of advanced technologies for detecting and characterizing micro- and nanoplastics, including Raman spectroscopy, infrared imaging, and mass spectrometry techniques. Researchers evaluated the capabilities and limitations of each method, particularly for identifying the smallest plastic particles that are most challenging to measure. The study emphasizes that improving detection at the nanoscale is essential for accurately assessing the environmental and health risks of plastic pollution.
Raman Spectroscopy Enhanced By Machine Learning For Effective Microplastic Detection In Aquatic Systems
Researchers explored combining Raman spectroscopy with machine learning techniques to improve microplastic detection and classification in aquatic systems. The study found that deep learning models, particularly convolutional neural networks, achieved high classification accuracy and significantly reduced reliance on labor-intensive manual spectral analysis for real-time environmental monitoring.