CE-UV/LIF Analysis of Organic Fluorescent Dyes for Detection of Nanoplastics in Water Quality Testing
Bibliographic record
Abstract
environmental studies that assessed the abundance of microplastics in different water columns [8].Release of nanoplastics from drinking water bottles was characterized by SEM, XPS, SPES and µ-Raman Analysis [9].Spherical organic nanoparticles from bottled water were collected effectively through a tangential flow ultrafiltration system [10].Polyethylene terephthalate nanoplastics collected from commercially bottled drinking water were detected with an average mean size of 88 nm; their concentration was estimated to be 10 8 particles/mL by nanoparticle tracking analysis [11].A new study has reported the levels of micro-and nano-particles released in carbonated beverage bottles range from 68 to 4.7×10 8 particles/L, potentially posing health risks to humans.Polypropylene bottles released more particles than polyethylene terephthalate and polyethylene bottles [12].The occurrence of micro-and nano-plastics (with particle diameters from 0.7 to 20 μm) in plastic bottled water has been assessed, and the median concentration was 359 ng L -1 .Polyethylene was the most detected polymer, while polyethylene terephthalate was found at the highest concentrations [13].The content of microplastic and nanoplastic particles in raw water, tap water, and drinking water was analyzed.Plastic particles were found in all water samples, with an average abundance ranging from 204 to 336 particles/L in raw water, from 22 to 33 particles/L in tap water, and from 25 to 73 particles/L in drinking water [14].Pyrolysis gas chromatography-mass spectrometry
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".