ArticleRSC advances2025
Deep-learning enabled rapid and low-cost detection of microplastics in consumer products following on-site extraction and image processing.
Article in RSC advances, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed.
- Developing and evaluating automated deep learning and human-in-the-loop vision-language systems for microplastic characterization.Scientific reports · 2026Article
- Deep Learning Integration in Optical Microscopy: Advancements and Applications.Microscopy research and technique · 2026Review
- Beyond Removal: A Critical Review of Microplastic Mass Flux, In-Plant Transformation, and Elimination in WWTPs.Molecules (Basel, Switzerland) · 2026Review
- Detection and identification of insect processed animal proteins in microscopic images using deep learning.Current research in food science · 2026Article
- Trapping tiny pollutants: SERS-driven strategies for microplastics and nanoplastics detection.iScience · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Microplastic (MP) contamination has become a major concern in recent times, posing a significant threat to the environment and human health. Existing techniques for MP detection require access to expensive and specialized microscopy setups and often demand long turnaround time and extensive labor. Herein, we propose a simple platform for MP detection, where MPs are extracted from salt, sugar, teabag, toothpaste and toothpowder samples, and imaged using a low-cost mobile phone-based microscopy setup. The extraction process involves the isolation of MPs from their matrices using the well-established density separation technique with ZnCl
Identifiers
What OpenQuestion holds
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.