ArticleScientific reports2023
A microfluidic approach for label-free identification of small-sized microplastics in seawater.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed.
- Microplastic Identification Methods for Microfluidic Applications: Towards Rapid Detection in Aquatic Environments.Polymers · 2026Review
- Miniaturization of Ocean Sensors Based on Optofluidic Technology: A Review.Sensors (Basel, Switzerland) · 2025Review
- MicroMetaSense: Coupling Plasmonic Metasurfaces with Fluorescence for Enhanced Detection of Microplastics in Real Samples.ACS sensors · 2025Article
- Microplastics and nanoplastics in the ocular environment: Pathways, toxic effects, and future challenges.Current research in toxicology · 2025Review
- Artificial Intelligence-Based Microfluidic Platform for Detecting Contaminants in Water: A Review.Sensors (Basel, Switzerland) · 2024Review
- Simple microfluidic devices forFrontiers in bioengineering and biotechnology · 2024Review
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Marine microplastics are emerging as a growing environmental concern due to their potential harm to marine biota. The substantial variations in their physical and chemical properties pose a significant challenge when it comes to sampling and characterizing small-sized microplastics. In this study, we introduce a novel microfluidic approach that simplifies the trapping and identification process of microplastics in surface seawater, eliminating the need for labeling. We examine various models, including support vector machine, random forest, convolutional neural network (CNN), and residual neural network (ResNet34), to assess their performance in identifying 11 common plastics. Our findings reveal that the CNN method outperforms the other models, achieving an impressive accuracy of 93% and a mean area under the curve of 98 ± 0.02%. Furthermore, we demonstrate that miniaturized devices can effectively trap and identify microplastics smaller than 50 µm. Overall, this proposed approach facilitates efficient sampling and identification of small-sized microplastics, potentially contributing to crucial long-term monitoring and treatment efforts.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.