Optimizing the Concentration of Nile Red for Screening of Microplastics in Bottled Water
Bibliographic record
Abstract
Increasing concern regarding the presence of microplastics in drinking water has led to a growing number of studies aimed at quantifying microplastics in water.In this work, we present an optimized procedure for the use of Nile red (NR) as a fluorescent staining agent for pre-screening of microplastics in bottled water.Positive and negative control experiments with NR concentrations ranging from 0.001 to 10 mg/L showed that the appropriate NR concentration is an important factor in obtaining representative particle counts.Non-optimized staining concentrations led to underestimation or overestimation of the particle count.In this study, the optimized NR staining concentration was found to be 0.1 mg/L.This method was successfully used to screen particles in seven different brands of bottled water consisting of both still and carbonated water, in both plastic and glass bottles.Particles larger than 100 µm were chemically characterized using attenuated total reflection-Fourier transform infrared spectroscopy (ATR-FTIR).Sixtyseven percent of these particles pre-screened with NR were confirmed to be polymers.Particles smaller than 100 µm were qualitatively analyzed using pyrolysis coupled with gas chromatography and mass spectroscopy (Py-GC-MS).Analysis of polymers between ~5-100 µm using Py-GC-MS confirmed that this smaller fraction generally mirrors the FTIR results for particles larger than l00 µm.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".