Tea Polyphenol EGCG Increases Nanoplastics Release from Plastic Cups but Mitigates Potential Detrimental Effects
Bibliographic record
Abstract
The presence of micro/nanoplastics in ecosystems and the potential for carry-over into daily human routines poses huge human health risks. While MNPs released from plastic packaging materials at different environmental conditions (e.g., pH, temperature) have been explored, the influence of real food ingredients (e.g., polyphenols) on plastic release has not been studied. Herein, for the first time, we investigated the effect of epigallocatechin gallate (EGCG), a relevant catechin polyphenol common to tea, on the release of nanoplastics from polystyrene (PS) cups during a heating process. We developed a novel surface-enhance Raman scattering sensor to quantify released nanoplastics in situ using EGCG-based luminescent metal phenolic network labeling strategies. The presence of added EGCG enhanced MNP release (P<0.05) when microwaved, more so than in boiling water relative to cold water control. We also observed that the higher amounts of added EGCG at the same pH and temperature caused higher amounts of nanoplastics due to the interaction of EGCG with nanoplastics. Reusing PS cups treated with EGCG in boiling water resulted in a gradual increase in nanoplastic release over 4 cycles. Of interest was the finding that EGCG also mitigated the detrimental effects of increased nanoplastics exposure in differentiated Caco-2 cell redox status in a concentration-dependent manner (P<0.05). These results imply that polyphenols as food and beverage ingredients may influence exposure to nanoplastics, but also may act to reduce nanoplastic cytotoxicity. This finding underlines the importance of broader consideration of food safety in public health discussions, focusing particularly on the composition of the food matrix and food processing and packaging applications that relate to different foods.
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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 | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".