Impact of Ball Milling on the Microstructure of Polyethylene Terephthalate
Bibliographic record
Abstract
Polyethylene terephthalate (PET) is a semi-crystalline polymer that finds broad use. Consequently, it contributes to the accumulation of plastics in the environment, warranting PET recycling technologies. Ball milling is a commonly used technique for the micronization of plastics before transformation. It has also recently been reported as an efficient mixing strategy for the enzymatic hydrolysis of plastics in moist-solid mixtures. However, the effect of milling on the microstructure of PET has not been systematically investigated. Thus, the primary objective of this study is to characterize the changes to the PET microstructure caused by various ball milling conditions. PET of different forms was examined, including pre- and post-consumer PET, as well as textiles. The material was treated to a range of milling frequencies and duration, before analysis of particle size, crystallinity by differential scanning calorimetry and powder X-ray diffraction, and morphology by scanning electron microscopy. Interestingly, our results suggest the convergence of crystallinity to ~30 % within 15 minutes of milling at 30 Hz. These results are consistent with an equilibrium between amorphous and crystalline regions of the polymer being established during ball milling. The combined data constitutes a reference guide for PET milling and recycling research.
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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 | 0.001 |
| 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".