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Papers
20 resultsShowing papers similar to Optical parameters extraction of soil and its microplastics contamination using terahertz spectroscopy
ClearTowards a fast and generalized microplastic quantification method in soil using terahertz spectroscopy
Researchers compared terahertz and near-infrared spectroscopy for quantifying microplastics in soil, finding that terahertz spectroscopy offered a faster and more accurate approach than NIR for distinguishing household microplastics from standard reference polymers in soil matrices.
Study on Rapid Quantitative Detection of Soil MPs Based on Terahertz Time-Domain Spectroscopy
Researchers developed a rapid method for detecting and quantifying microplastics in soil using terahertz time-domain spectroscopy combined with machine learning algorithms. The classification models achieved high accuracy in identifying different types of microplastics including polyethylene, polystyrene, and polypropylene. The study suggests that terahertz spectroscopy could provide a faster and more efficient alternative to current methods for monitoring microplastic contamination in agricultural soils.
Development of a Compact and Portable Terahertz Imaging System for Industrial Applications
Researchers developed a compact, portable terahertz imaging device suitable for use outside the laboratory, demonstrating its ability to detect microplastics in soil among a range of other applications. While microplastic detection is one of several uses tested, the availability of low-cost, field-deployable detection technology could support faster and broader environmental monitoring of microplastic contamination.
Characterizations of high-density polyethylene by terahertz time-domain spectroscopy
Researchers characterized the optical properties of high-density polyethylene (HDPE) particles using terahertz time-domain spectroscopy and found the method can distinguish different particle sizes and filler contents. The technique can also detect how HDPE adsorbs other substances onto its surface. Terahertz spectroscopy could be developed as a rapid, non-destructive tool for identifying and characterizing HDPE microplastics in environmental samples.
Microplastic detection in soil by THz Time-Domain hyperspectral imaging combined with unsupervised learning analysis
Researchers applied terahertz time-domain hyperspectral imaging combined with multiple unsupervised machine-learning algorithms to detect and spatially map low-density polyethylene microplastics in soil, demonstrating that all five methods consistently separated plastic from soil without requiring labeled training data, establishing a reference-free detection approach.
Detection of Microplastic in Salts Using Terahertz Time-Domain Spectroscopy
Researchers demonstrated that terahertz spectroscopy can detect microplastics embedded in table salt at different concentrations. This technology could offer a new non-destructive method for screening food products for microplastic contamination.
Vis-NIR spectroscopy based rapid and non-destructive method to quantitate microplastics: An emerging contaminant in farm soil
Researchers developed a rapid, non-destructive method using visible and near-infrared spectroscopy to quantify microplastics in farm soil. The study suggests this approach could overcome the limitations of current extraction-based methods, which are time-consuming and prone to errors and biases.
A novel and simple method for measuring nano/microplastic concentrations in soil using UV-Vis spectroscopy with optimal wavelength selection
Researchers developed a simple UV-Vis spectroscopy method for measuring nano- and microplastic concentrations in soil, using optimized wavelength combinations to account for interference from soil particles. The study demonstrated a linear relationship between spectroscopic measurements and actual plastic concentrations, offering a potentially practical tool for monitoring plastic contamination across different soil types.
A novel way to rapidly monitor microplastics in soil by hyperspectral imaging technology and chemometrics
Hyperspectral imaging combined with chemometrics was demonstrated as a novel way to rapidly detect and map multiple types of microplastics in soil samples, identifying particles of different polymer types based on their spectral signatures. The approach could enable faster and more spatially detailed monitoring of microplastic contamination in agricultural and environmental soils.
Research on Identification and Classification Methods for Soil Microplastics in Hyperspectral Detection
Hyperspectral imaging was tested as a rapid, large-area detection method for identifying and classifying microplastics in soil, offering an alternative to time-consuming particle-by-particle Raman or FTIR spectroscopy. The approach could allow researchers to map microplastic distribution across soil samples far more efficiently. Faster detection technology is important for expanding the geographic scope of soil microplastic monitoring and for assessing contamination in agricultural land.
Detection of microplastic pollution in top soils using optical reflectance spectroscopy from the ultraviolet to shortwave infrared: a review
This review examined the potential of optical reflectance spectroscopy across the ultraviolet to shortwave infrared range as a detection method for microplastic pollution in soils. Researchers assessed the current state of spectroscopic approaches for soil microplastic identification, highlighting both the promise of this non-destructive technique and the key challenges that must be overcome for reliable field and laboratory application.
Analysis of microplastics in soil samples by using a thermal decomposition method
Researchers tested thermal decomposition as a method for extracting and identifying microplastics in soil samples, a particularly challenging matrix because organic matter interferes with optical detection methods. The approach showed promise for detecting and quantifying plastic content in complex soil environments.
Automated identification and quantification of invisible microplastics in agricultural soils
Researchers developed an automated method combining laser direct infrared and FTIR spectroscopy to identify microplastics in agricultural soils, revealing that particles smaller than 500 micrometers account for over 96% of soil microplastics that are invisible to traditional visual inspection.
Microplastic Analysis in Soil Using Ultra-High-Resolution UV–Vis–NIR Spectroscopy and Chemometric Modeling
Researchers tested a new method using UV-visible-near infrared spectroscopy combined with machine learning to identify microplastics in soil samples. They found the technique could rapidly and accurately distinguish between different plastic polymers and natural soil particles. The study offers a promising alternative to current labor-intensive identification methods, potentially making large-scale microplastic soil monitoring more practical.
Study on detection method of microplastics in farmland soil based on hyperspectral imaging technology
Researchers developed a method using hyperspectral imaging and machine learning to rapidly detect and classify different types of microplastics in farmland soil. The technology achieved high accuracy in identifying common plastic types like polyethylene and polypropylene in soil samples. Better detection tools like this are essential for monitoring microplastic contamination in agricultural land and understanding its potential impact on food safety.
Identification and characterization of various plastics using THz-spectroscopy
Researchers used terahertz spectroscopy, which has reached spatial resolutions of a few micrometres and interacts with molecular vibrations without ionizing samples or damaging DNA, to identify and characterize various plastic types, demonstrating the technique's potential for building materials databases and biological imaging applications.
Rapid Detection of Microplastics in Plastic-covered Soil Using FT-NIR and ATR-FTIR Spectral Data Fusion
Scientists developed a new method to quickly detect tiny plastic particles in farm soil by combining two different light-based detection techniques. This method can accurately measure microplastic pollution in agricultural fields where plastic covers are used for growing crops. This matters because microplastics in farm soil can potentially enter our food chain through the fruits and vegetables we eat.
Critical evaluation of hyperspectral imaging technology for detection and quantification of microplastics in soil
Researchers evaluated whether hyperspectral imaging technology can reliably detect and quantify microplastics in soil under varying real-world conditions. They found that near-infrared imaging generally works well but is significantly affected by factors like soil moisture, microplastic color, and particle size. The study recommends sorting microplastics by size before analysis and further research into moisture effects, providing the first comprehensive evaluation of this emerging detection technology for soil monitoring.
Microplastic detection in arable soil using a 3D Laser Scanning Confocal Microscope coupled with a Machine-Learning Algorithm
Researchers used 3D laser scanning confocal microscopy paired with machine learning to detect microplastics in agricultural soil. The method successfully identified low-density polyethylene particles from mulching films, providing a faster and more precise tool for tracking plastic contamination in farmland.
Application of hyperspectral imaging technology in the rapid identification of microplastics in farmland soil
Researchers applied hyperspectral imaging technology combined with machine learning to rapidly screen and classify microplastics in farmland soil samples, demonstrating an efficient non-destructive identification method for soil microplastic contamination.