Fluorescence Detection of Zinc Oxide Nanoparticles in Water Contamination Analysis based on Surface Reactivity with Porphyrin
Bibliographic record
Abstract
A simple rapid analytical method for determining the concentration of ZnO nanoparticles in aqueous dispersion has been developed by adding porphyrin (TCPP) as a fluorophore into the water sample for fluorescence analysis. Quenching of the emission intensity at 650 nm provides a Stern-Volmer plot with adequate sensitivity for the detection of ZnO nanoparticles from 0.15 mg/mL up to 1.5 mg/mL. A new emission peak at 605 nm can be attributed to the formation of a unique ZnO-TCPP complex. This unique emission peak is good for both identification and quantitation of ZnO nanoparticles at low concentrations down to 0.0015 mg/mL. The new method affords a linear dynamic range up to 1.2 mg/mL.
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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 | 0.000 | 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".