Plastic Analysis with a Plasmonic Nano-Gold Sensor Coated with Plastic-Binding Peptides
Bibliographic record
Abstract
Contamination with plastics of small dimensions (<1 µm) represents a health concern for many terrestrial and aquatic organisms. This study examined the use of plastic-binding peptides as a coating probe to detect various types of plastic using a plasmon nano-gold sensor. Plastic-binding peptides were selected for polyethylene (PE), polyethylene terephthalate (PET), polypropylene (PP), and polystyrene (PS) based on the reported literature. Using nAu with each of these peptides to test the target plastics revealed high signal, at 525/630 nm, suggesting that the target plastic limited HCl-induced nAu aggregation. Testing with other plastics revealed some lack of specificity but the signal was always lower than that of the target plastic. This suggests that these peptides, although reacting mainly with their target plastic, show partial reactivity with the other target plastics. By using a multiple regression model, the relative levels of a given plastic could be corrected by the presence of other plastics. This approach was tested in freshwater mussels caged for 3 months at sites suspected to release plastic materials: in rainfall overflow discharges, downstream a largely populated city, and in a municipal effluent dispersion plume. The data revealed that the digestive glands of the mussels contained higher levels of PP, PE, and PET plastic particles at the rainfall overflow and downstream city sites compared to the treated municipal effluent site. This corroborated earlier findings that wastewater treatment could remove nanoparticles, at least in part. A quick and inexpensive screening test for plastic nanoparticles in biological samples with plasmonic nAu-peptides is proposed.
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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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".