Emerging investigator series: unpacking PET: comparative analysis of leachable and extractable contaminants from virgin and recycled polyethylene terephthalate bottles and textiles
Bibliographic record
Abstract
Plastics, particularly polyethylene terephthalate (PET), are widely used as food contact materials, textiles, and toys. However, their widespread use and potential for human exposure raise environmental and health concerns, particularly regarding the leaching of chemical additives. This study assessed hazardous plastic additives and non-intentionally added substances (NIAS) leached from paired virgin and recycled PET bottles (soda and water) purchased from Michigan and California and from textiles (toys, pillows, and clothing) acquired online or in stores in Michigan and Oregon. Results showed differences in contaminant profiles and concentrations between PET types and products. A total of 12 persistent, mobile, and toxic (PMT) additives, six organophosphate esters (OPEs), and 15 NIAS were detected. Notably, recycled PET (rPET) bottles consistently contained benzene, while virgin PET had higher ethylene glycol and 2-methyl-1,3-dioxolane levels. Additionally, OPEs were detected more frequently in rPET, indicating recycling as a contamination pathway. Geographically distinct contaminant profiles were evident, with Michigan bottles exhibiting elevated benzaldehyde, while California bottles showed higher diethylene glycol levels, suggesting differing manufacturing practices. Textiles exhibited distinct contamination profiles, highlighting a distinct exposure pathway for watersheds through laundry processes. Bioactivity assays with PET product extracts revealed moderate to high hormone receptor antagonism but no clear association with PET type, indicating potential hazardous effects from both virgin and recycled PET products. This study highlights the necessity of continued monitoring of contaminants in PET, including non-intentionally added substances and PMT plastic additives that are not currently regulated.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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 teacher head, 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".