Machine learning based workflow for (micro)plastic spectral reconstruction and classification DOI
Yanlong Liu, Ziwei Zhao,

Chunyang Hu

et al.

Chemosphere, Journal Year: 2024, Volume and Issue: 369, P. 143835 - 143835

Published: Dec. 1, 2024

Language: Английский

Microplastics and pharmaceuticals from water and wastewater: Occurrence, impacts, and membrane bioreactor-based removal DOI
Dong Nguyen, Minh‐Ky Nguyen, Quoc-Minh Truong

et al.

Separation and Purification Technology, Journal Year: 2025, Volume and Issue: unknown, P. 131489 - 131489

Published: Jan. 1, 2025

Language: Английский

Citations

2

Antibiotic resistant bacteria and antibiotic resistance genes as contaminants of emerging concern: Occurrences, impacts, mitigations and future guidelines DOI

Jeffrey Saúl Cedeño-Muñoz,

Sesan Abiodun Aransiola,

Kondakindi Venkateswar Reddy

et al.

The Science of The Total Environment, Journal Year: 2024, Volume and Issue: 952, P. 175906 - 175906

Published: Sept. 1, 2024

Language: Английский

Citations

6

Synergy between Artificial Intelligence and Hyperspectral Imagining—A Review DOI Creative Commons
Svetlana N. Khonina, Nikolay L. Kazanskiy, Ivan Oseledets

et al.

Technologies, Journal Year: 2024, Volume and Issue: 12(9), P. 163 - 163

Published: Sept. 13, 2024

The synergy between artificial intelligence (AI) and hyperspectral imaging (HSI) holds tremendous potential across a wide array of fields. By leveraging AI, the processing interpretation vast complex data generated by HSI are significantly enhanced, allowing for more accurate, efficient, insightful analysis. This powerful combination has to revolutionize key areas such as agriculture, environmental monitoring, medical diagnostics providing precise, real-time insights that were previously unattainable. In instance, AI-driven can enable precise crop monitoring disease detection, optimizing yields reducing waste. this technology track changes in ecosystems with unprecedented detail, aiding conservation efforts disaster response. diagnostics, AI-HSI could earlier accurate improving patient outcomes. As AI algorithms advance, their integration is expected drive innovations enhance decision-making various sectors. continued development these technologies likely open new frontiers scientific research practical applications, accessible tools wider range users.

Language: Английский

Citations

5

Quantitative methodology for poly (butylene adipate-co-terephthalate) (PBAT) microplastic detection in soil and compost DOI Creative Commons
Yvan D. Hernandez-Charpak,

Harshal J. Kansara,

Jeffrey S. Lodge

et al.

Environmental Science and Pollution Research, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 31, 2025

With the increasing use of biodegradable plastics in agriculture and food packaging, it has become increasingly important to assess effects their fragmentation mineralization environment (i.e., soil, compost). PBAT is a polyester widely used mulch films that are intended fragment mineralize soil. To study these effects, novel methodologies needed quantify microplastics diverse environments. This work seeks answer whether gas chromatography mass spectrometry (GCMS) can be as tool A method was developed allows soil extraction by ultrasonication GCMS quantification after fatty acid methyl ester derivatization. validate method, an industrial compost degradation experiment carried out evidence weight loss film micro- nano-plastic generated from them. The presented improved existing resolution by, at least, one order magnitude compared reported methods. In conclusion, novel, simple, affordable, reproducible methodology for microplastic detection improving limits quantification. tested on experiment, demonstrating ability trace totality plastic over time, evidencing consumed environment.

Language: Английский

Citations

0

A Review of Materials for the Removal of Micro- and Nanoplastics from Different Environments DOI Creative Commons
Christian Ebere Enyoh,

Arti Devi,

Tochukwu Oluwatosin Maduka

et al.

Micro, Journal Year: 2025, Volume and Issue: 5(2), P. 17 - 17

Published: April 9, 2025

Microplastics (MPs) and nanoplastics (NPs) have emerged as persistent environmental pollutants, posing significant ecological human health risks. Their widespread presence in aquatic, terrestrial, atmospheric ecosystems necessitates effective removal strategies. Traditional methods, including filtration, coagulation, sedimentation, demonstrated efficacy for larger MPs but struggle with nanoscale plastics. Advanced techniques, such adsorption, membrane photocatalysis, electrochemical shown promising results, yet challenges remain scalability, cost-effectiveness, impact. Emerging approaches, functionalized magnetic nanoparticles, AI-driven detection, laser-based remediation, present innovative solutions tackling MP NP contamination. This review provides a comprehensive analysis of current emerging strategies, evaluating their efficiency, limitations, future prospects. By identifying key research gaps, this study aims to guide advancements sustainable scalable microplastic technologies, essential mitigating implications.

Language: Английский

Citations

0

Improved neural networks for the classification of microplastics via inferior quality Raman spectra DOI

Weixiang Huang,

Jiajin Chen,

Hao Xiong

et al.

Talanta, Journal Year: 2025, Volume and Issue: 289, P. 127756 - 127756

Published: Feb. 18, 2025

Language: Английский

Citations

0

The Impact of Innovative Cities Construction on Air Pollution: Evidence from China DOI
Peng Yang, Xingyi Zhang,

Wenya Lv

et al.

Applied Spatial Analysis and Policy, Journal Year: 2025, Volume and Issue: 18(1)

Published: Feb. 20, 2025

Language: Английский

Citations

0

Microplastic Pollution in Terrestrial Systems: Sources and Implications for Soil Functioning and Plant Performance DOI

Nafiaah Naqash,

Krishna Kumar Yadav,

Abdul Saddique Shaik

et al.

Water Air & Soil Pollution, Journal Year: 2025, Volume and Issue: 236(3)

Published: Feb. 24, 2025

Language: Английский

Citations

0

Enhanced spectral signatures with Ag nanoarrays in hyperspectral microscopy for CNN-based microplastics classfication DOI Creative Commons
Xinwei Dong, Zhao Xu, Jianing Xu

et al.

Frontiers in Chemistry, Journal Year: 2025, Volume and Issue: 13

Published: March 21, 2025

Microplastics are a pervasive pollutant in aquatic ecosystems, raising critical environmental and public health concerns driving the need for advanced detection technologies. Microscopic hyperspectral imaging (micro-HSI), known its ability to simultaneously capture spatial spectral information, has shown promise microplastic analysis. However, widespread application is hindered by limitations such as low signal-to-noise ratios (SNR) reduced sensitivity smaller particles. To address these challenges, this study investigates use of Ag nanoarrays reflective substrates micro-HSI. The localized surface plasmon resonance (LSPR) effect enhances resolution suppressing background reflections isolating reflection bands from interference. This improvement results significantly increased SNR more distinct features. When analyzed using 3D-2D convolutional neural network (3D-2D CNN), integration improved classification accuracy 90.17% 98.98%. These enhancements were further validated through Support Vector Machine (SVM) analyses, demonstrating robustness reliability proposed approach. demonstrates potential combining with CNN models enhance micro-HSI performance, offering novel effective solution precise microplastics advancing chemical analysis, monitoring, related fields.

Language: Английский

Citations

0

Plasmonic substrates enhanced micro-hyperspectral imaging for AI-based recognition of microplastics in water DOI
Xinwei Dong, Zhao Xu, Fuxin Zheng

et al.

Microchemical Journal, Journal Year: 2025, Volume and Issue: unknown, P. 113465 - 113465

Published: March 1, 2025

Language: Английский

Citations

0