Identification and Quantification of Microplastics Using Nile Red Staining
Bibliographic record
Abstract
Plastic is a very useful and versatile product, however extensive use and unchecked disposal has resulted in significant global impacts. Microplastic (0.1 µm–5 mm) is particularly problematic and is a widespread pollutant impacting aquatic ecosystems. The accumulation of microplastics produces negative repercussions such as aesthetic and economic impact and most importantly adverse biological and ecological effects. Existing identification and quantification techniques such as Raman spectroscopy, Pyrolysis-gas chromatography with mass spectrometry and FT-IR spectroscopy are time consuming and require expensive instruments. The aim of this research is to develop a rapid fluorescent staining procedure for microplastic quantification using a fluorescent dye, Nile Red. Developing a fluorescent staining procedure will provide a rapid way of differentiating microplastics from the other natural materials, enabling easier and more accurate quantification of microplastics. The first step would be the formation of microplastics from common materials such as freezer bags (polyethylene), bottle caps (polypropylene) or food containers (polystyrene). These will be used as standards for further tests including the selection of a suitable organic solvent that would not degrade or stain the filter paper but effectively stain the microplastics. Stained microplastics will be irradiated with blue or green light causing fluorescence, which can then be detected using red filter. This method will be compared to traditional methods such as Raman spectroscopy and brightfield microscopy and then be applied to the samples extracted from the North Saskatchewan River. The results from the study will help in efficient sample processing and understanding microplastic contamination in our environment. Faculty Mentor: Dr. Matthew Ross Discipline: Chemistry
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".