Isolation and Quantification of Polystyrene Nanoplastics in Tissues by Low Pressure Size Exclusion Chromatography
Bibliographic record
Abstract
Ecotoxicity investigations of plastic nanoparticles (NPs) should pay more attention to their ability to pass barriers, accumulate, and initiate toxicity in cells. The purpose of this study was to develop a simple size exclusion chromatography (SEC) methodology to measure plastic NPs in biological tissues. A SEC column was prepared using a high-resolution gel for large macromolecules to separate plastic NPs from the protein/lipid pools in tissues. It was necessary to prepare the samples in high salt and non-ionic detergent (0.5 M NaCl and 0.2% Tween-20) and apply 0.2% Tween-20 containing 14 mM NaCl for the elution buffer to limit proteins adsorption to NPs. This methodology was able to resolve 50 and 100 nm polystyrene NPs from the protein/lipid pools in tissue homogenates. The fluorescent dye neutral red (NR) was also used for transparent NPs. Moreover, a sample fractionation step was also proposed for plastic NPs concentration using a salting-out methodology with saturated NaCl (5 M) and acetonitrile. Polystyrene NPs partition in acetonitrile, which were further analyzed by SEC. This methodology was tested in two case studies with clams collected in a high boat traffic (harbor) area and with caged freshwater mussels downstream of a large urban area. Although the present methodology was developed with polystyrene NPs it should be amenable to other plastic polymers that react with the NR fluorescent probe.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".