Analysis of microplastics and nanoplastics emerged from polyethylene bags and polyethylene terephthalate bottles by an artificial intelligence-enabled tool
Bibliographic record
Abstract
This study presents a comprehensive and comparative analysis of the emergence of micro- and nanoplastics (MNPs) from polyethylene (PE) plastic bags and polyethylene terephthalate (PET) water bottles. We subjected these polymers to simulated mechanical and photodegradation conditions in an isolated chamber for 12 weeks to understand their environmental implications and degradation mechanisms. We analyzed 614 and 3,924 plastic particles that emerged from PE and PET, respectively, using an artificial intelligence (AI) enhanced automatic annotation tool (FastSAM) focusing on MNP’s particle count, size distribution, and morphology. This innovative approach combines comprehensive simulation of environmental conditions with AI-enabled image analysis, providing detailed insight into the relative contributions of PE and PET products to plastic pollution. Our findings indicate that PET fragments more readily into smaller particles, with a higher proportion of nanoplastics (57.6%) than PE (24.9%). The concentration of the emerged particles was found to be 4.17 million particles/L (0.07 ppm) for PE and 27.8 million particles/L (0.18 ppm) for PET. Characterization techniques, including dynamic light scattering (DLS), scanning electron microscopy (SEM), Fourier-transform infrared spectroscopy (FTIR), and X-ray photoelectron spectroscopy (XPS) were used to examine both bulk plastics and the MNPs that emerged from them. • Compared to PE, PET releases more plastic particles, which are mostly nanoplastics • SEM, FTIR, and XPS reveal morphological and chemical changes in degraded plastics • AI-enabled analysis demonstrates clear differences in particle size and morphology • MNPs exhibit distinct, source-dependent morphological attributes
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 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".