Rapid On-Site and Sensitive Detection of Microplastics Using Zirconium(IV)-Assisted SERS Label
Bibliographic record
Abstract
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 a rapid, on-site, and sensitive method for detecting microplastics in agri-food and environmental systems is important for assessing and minimizing their potential risks. In this study, we developed a novel surface-enhanced Raman spectroscopy (SERS) technique for the rapid, on-site, and ultrasensitive detection of microplastics. Our innovative technique incorporated Zr 4+ -assisted SERS label strategies, utilizing rhodamine B as a Raman reporter to improve microplastics analysis. By utilizing Zr 4+ -assisted SERS label approaches, we can achieve qualitative and ultrasensitive quantification of 10 μm polystyrene microplastics (PSMPs) at concentrations as low as 0.1 ppm with a detection limit of 1 ppb. Furthermore, this approach allows for detecting microplastics in real-world scenarios, with recovery rates exceeding 90% for polystyrene microplastic concentrations ranging from 5 to 30 ppm in tap water systems. When integrated with a portable Raman spectrometer, this innovative approach showcases the rapid, on-site, accurate, and sensitive detection of microplastics and has great potential for analyzing various types of microplastics in agri-food and environmental systems.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".