A smartphone label-free and automated thermo-analytical method based on image analysis to detect microplastics DOI
Federico Figueredo, Mónica Mosquera,

Francisco Di Lullo

et al.

The Science of The Total Environment, Journal Year: 2024, Volume and Issue: 958, P. 178114 - 178114

Published: Dec. 20, 2024

Language: Английский

Rapid On-Site and Sensitive Detection of Microplastics Using Zirconium(IV)-Assisted SERS Label DOI

Haoming Yang,

Haoxin Ye, Yan Song

et al.

Journal of Agricultural and Food Chemistry, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 2, 2025

Microplastics have emerged as significant pollutants in terrestrial and marine ecosystems, with their accumulation posing a threat to human health through biomagnification along the food chain. Developing rapid, on-site, sensitive method for detecting microplastics agri-food environmental systems is important assessing minimizing potential risks. In this study, we developed novel surface-enhanced Raman spectroscopy (SERS) technique ultrasensitive detection of microplastics. Our innovative incorporated Zr4+-assisted SERS label strategies, utilizing rhodamine B reporter improve analysis. By approaches, can achieve qualitative quantification 10 μm polystyrene (PSMPs) at concentrations low 0.1 ppm limit 1 ppb. Furthermore, approach allows real-world scenarios, recovery rates exceeding 90% microplastic ranging from 5 30 tap water systems. When integrated portable spectrometer, showcases accurate, has great analyzing various types

Language: Английский

Citations

1
Exploring sustainable strategies for mitigating microplastic contamination in soil, water, and the food chain: A comprehensive analysis DOI Creative Commons

Udaratta Bhattacharjee,

Khanindram Baruah, Maulin P. Shah

et al.

Environmental Chemistry and Ecotoxicology, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 1, 2025

Language: Английский

Citations

1
Integrating Metal–Phenolic Networks-Mediated Separation and Machine Learning-Aided Surface-Enhanced Raman Spectroscopy for Accurate Nanoplastics Quantification and Classification DOI
Haoxin Ye, Shiyu Jiang,

Yan Yan

et al.

ACS Nano, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 16, 2024

Increasing accumulation of nanoplastics across ecosystems poses a significant threat to both terrestrial and aquatic life. Surface-enhanced Raman scattering (SERS) is an emerging technique used for detection. However, the identification classification using SERS faces challenges regarding sensitivity accuracy as are sparsely dispersed in environment. Metal-phenolic networks (MPNs) have potential rapidly concentrate separate various types sizes nanoplastics. combined with machine learning may improve prediction accuracy. Herein, we report integration MPNs-mediated separation learning-aided methods accurate high-precision quantification nanoplastics, which tailored include complete region characteristic peaks diverse contrast traditional manual analysis spectra on singular peak. Our customized system (e.g., outlier detection, classification, quantification) allows detectable (accuracy 81.84%), > 97%), sensitive (polystyrene (PS), poly(methyl methacrylate) (PMMA), polyethylene (PE), poly(lactic acid) (PLA)) down ultralow concentrations (0.1 ppm) well 92%) nanoplastic mixtures at subppm level. The effectiveness this approach substantiated by its ability discern between different detect samples natural water systems.

Language: Английский

Citations

6
Morphological and chemical analysis of indoor airborne microplastics: implications for human health in Ahvaz, Iran DOI
Neda Kaydi, Sahand Jorfi, Afshin Takdastan

et al.

Environmental Geochemistry and Health, Journal Year: 2025, Volume and Issue: 47(4)

Published: March 1, 2025

Language: Английский

Citations

0
Computational polarized holography for automatic monitoring of microplastics in scattering aquatic environments DOI Creative Commons
Jianqing Huang, Shuo Zhu, Yuxing Li

et al.

APL Photonics, Journal Year: 2025, Volume and Issue: 10(3)

Published: March 1, 2025

Automatic monitoring of microplastic (MP) contamination in aquatic ecosystems is crucial for effective management and mitigation strategies. However, this task presents significant challenges due to the dynamic 3D distribution MPs light scattering aqueous phase. Traditional MP detection methods are limited volumetric imaging anti-scattering capability, often requiring cumbersome manual processing analysis. In study, we develop an integrated system based on computational polarized holography, which offers unique advantages automation, multifunctionality, affordability. As demonstrated with proof-of-concept experiments, our enables accurate efficient tracking across extended volume, facilitating high-throughput addition, proposed hybrid de-scattering algorithm substantially improves image quality even when characterizing milk solutions. Furthermore, unsupervised clustering method developed identify classify different their multimodal features without need annotation. Although experiments were implemented laboratory, results demonstrate robust efficiency material-dependent sensitivity system. It opens up new opportunities on-site continuous pollution ecosystems, contributing significantly sustainable environmental management.

Language: Английский

Citations

0
Impact of Plastic Contaminants on Marine Ecosystems and Advancement in the Detection of Micro/Nano Plastics: A Review DOI Creative Commons

Harish Farale,

K B Sreevidhya,

Ayyappa Bathinapatla

et al.

Journal of Hazardous Materials Advances, Journal Year: 2025, Volume and Issue: unknown, P. 100736 - 100736

Published: April 1, 2025

Language: Английский

Citations

0
A smartphone label-free and automated thermo-analytical method based on image analysis to detect microplastics DOI
Federico Figueredo, Mónica Mosquera,

Francisco Di Lullo

et al.

The Science of The Total Environment, Journal Year: 2024, Volume and Issue: 958, P. 178114 - 178114

Published: Dec. 20, 2024

Language: Английский

Citations

0