Selection of an appropriate fluorescent reference material to assess microplastic recovery in natural waters
Bibliographic record
Abstract
Abstract Monitoring programs have been recently proposed to characterize the presence of microplastics (MPs) in source and treated drinking waters. Successful implementation of such programs will require the development of standardized sampling protocols that can address MP particles < 20 µm, representing the most abundant sizes and relevance in terms of potential human health impacts. Validation of sampling methodologies typically involve spike and recovery trials (to serve as positive controls). To-date, no known methods have been proposed for the production of fluorescent reference materials that are representative of the shape and size distribution of environmental MPs (excluding tire and rubber particles). In this study, an optimal fluorescence reference suspension was developed for use in spike and recovery assessments of microplastic sampling methods when considering source and treated drinking waters. Aqueous particle suspensions were prepared using both commercially available microspheres and lab-prepared MP fragments, such that relative standard deviations (RSD) were calculated within size bins. Nile red-stained polyethylene terephthalate (PET) fragments were identified as an optimal reference material based on an RSD of 2.5% among replicate spikes. No change in fluorescence intensity was observed for Nile red-stained PET fragments following a process to remove extraneous (non-plastic) particles that incorporated a Fenton’s reagent and enzyme-based methodology. In addition, fluorescence intensity of Nile red-stained PET fragments in solution was observed to be stable over a four-month period. As such, it is anticipated that fluorescent PET fragments may be employed in future studies where assessment of microplastic recovery is desired.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".