Are There Nanoplastics in Your Personal Care Products?
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Fragmentation of plastic debris and the commercial use of plastic microbeads have led to the widespread distribution of microplastics in natural environments. Several studies have reported on the occurrence and toxicity of microplastics in soils and waters; however, because of methodological challenges, the presence and impact of nanoplastics (<100 nm) in natural systems have been largely ignored. Microbeads used in consumer products such as scrubs and shampoos are processed by mechanical means that may lead to their fragmentation into potentially more hazardous nanoplastics. In this study, three commercial facial scrubs containing polyethylene microbeads (∼0.2 mm diameter) were examined to verify whether they contained nanoplastics. Particulates in the scrubs were fractionated using sequential filtration to isolate <100 nm particles. Scanning electron microscopy was used to confirm the presence of nanoparticles ranging in size from 24 ± 6 to 52 ± 14 nm. X-ray photoelectron spectroscopy and Fourier transform infrared spectroscopy were used to confirm that the identified nanoparticles consisted of polyethylene. This study confirms the (unexpected) presence of nanoplastics in personal care products containing polyethylene microbeads and highlights the need for further studies to characterize the release and distribution of nanoplastic litter in natural aquatic and soil environments.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".