Single particle-resolution fluorescence microscopy of nanoplastics
Bibliographic record
Abstract
Abstract Understanding of nanoplastic prevalence and toxicology is limited by imaging challenges resulting from their small size. Fluorescence microscopy is widely applied to track and identify microplastics in laboratory studies and environmental samples. However, conventional fluorescence microscopy, due to diffraction, lacks the resolution to precisely localize nanoplastics in tissues, distinguish them from free dye, or quantify them in environmental samples. To address these limitations, we developed techniques to label nanoplastics for imaging with Stimulated Emission Depletion (STED) microscopy to achieve resolution at an order of magnitude superior to conventional fluorescence microscopy. These techniques include (1) passive sorption; (2) swell incorporation; and (3) covalent coupling of STED-compatible fluorescence dyes to nanoplastics. We demonstrate that our labeling techniques, combined with STED microscopy, can be used to resolve nanoplastics of different shapes and compositions as small as 50 nm. The longevity of the dye labeling is demonstrated in different media and conditions of biological and environmental relevance. We also test STED imaging of nanoplastics in exposure experiments with the model worm C. elegans . These techniques will allow more precise localization and quantification of nanoplastics in complex matrices. Synopsis We show that Stimulated Emission Depletion (STED) microscopy can be used to image single nanoplastics of different compositions and shapes. This will allow researchers to study environmentally-relevant nanoplastics and their interactions with organisms in relevant exposure scenarios. TOC Graphic
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".