Kinetic analysis and modelling of thermal degradation of perspex (PMMA) and perspex blend plastic waste
Bibliographic record
Abstract
Abstract The thermal decomposition of pure perspex and a mixture of 50% perspex and 50% poly(ethylene terephthalate; PET) was carried out between 295 and 325°C using a thermogravimetric analyser (TGA) in air and nitrogen (N2) atmosphere. The weight losses of decomposition products were measured during these experiments. The thermal degradation process is slower in inert atmosphere than air, where oxidation reaction expedites the decomposition process. Kinetic rate constants (k), pre‐exponential factor (A) and activation energy (E) for both pure prespex and a blend of perspex/PET were calculated for both air and N2 conditions. The thermal degradation process followed a third‐order reaction in air and second‐order in N2. A second‐order (n = 2) model for the pyrolytic process based on simultaneous reactions was developed using experimental data for pure and blend. The pyrolytic products are gases, liquids, waxes, aromatics and char, which can be ultimately used as raw material and fuel in various applications. It is important to note that the addition of PET to perspex was found to suppress/inhibit the decomposition of perspex compared with pure perspex. Pre‐exponential factor (A) and activation energy (E) values support such an observation. © 2012 Canadian Society for Chemical Engineering
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".