Quantitative and rapid detection of nanoplastics labeled by luminescent metal phenolic networks using surface enhanced Raman scattering
Bibliographic record
Abstract
The rising incidence of nanoplastics contamination in environmental ecosystems has led to substantial health risks. Traditional analysis methods are suboptimal due to their inability to efficiently analyze nanoplastics at low concentrations and time-consuming operations. Herein, we developed an innovative strategy, employing luminescent metal–phenolic networks (L-MPNs) coupled with surface-enhanced Raman spectroscopy (SERS) to separate and label nanoplastics, thus facilitating rapid, sensitive and quantitative detection of nanoplastics. We used L-MPNs composed of zirconium ions, tannic acid and rhodamine B, to uniformly label diverse sizes (50-500 nm) and types of nanoplastics (i.e., polystyrene, polymethyl methacrylate, polylactic acid). Rhodamine B, serving as a Raman reporter in L-MPNs-based SERS tags can offer sufficient sensitivity for trace measurement of nanoplastics and L-MPNs labeling can also facilitate separation of nanoplastics from liquid medium. By using a portable Raman instrument, our method offers cost-effective, rapid, and field-deployable detection features with excellent sensitivity in nanoplastic analysis with a limit of detection of 0.1 ppm. Moreover, this study provides a highly promising strategy for the robust and sensitive analysis of a wide range of particle analytes through the effective labeling performance of L-MPNs when coupled with SERS techniques.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".