PET pyrolysis: Kinetics of a‐graphite
Bibliographic record
Abstract
Abstract One of the primary issues nowadays is plastic waste management, where the low recycling rates and accumulation of waste plastic show an exponentially increasing trend with urbanization. Especially, the concern is high with the PET waste generated from food packaging industries. Thus, new technologies are required for waste refining. Pyrolysis is one such technique that will restrict the emission possibilities obvious with the incineration and convert plastic into some value‐added end products. However, large scale implementation of pyrolysis technology for plastic waste management requires a thorough understanding of its degradation kinetics along with pyrolysis index (PI) calculation. Here a thorough analysis of the degradation kinetics of PET waste was done with established models to evaluate the activation energy for the reactions. Among all the conventional models discussed, Coats‐Redfern shows a 0.95–0.98 R2 value during model fitting. The activation energies were varied from 133.6 to 241.8 kJ/mol. PI value was evaluated at different temperatures to assess lower energy utilization during designing and scaling up the process. The value was found to be maximum at 500°C indicating the scalability of the process at this temperature with lower energy utilization. Qualitative predictions on the production of amorphous graphite oxide (a‐graphite) were validated through FTIR, FESEM, Raman, XRD, and UV spectroscopy analyses.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".