Isothermal co‐pyrolytic kinetics investigation of polystyrene/polymethyl methacrylate blended Bakelite
Bibliographic record
Abstract
Abstract The widespread use of Bakelite, polystyrene (PS), and polymethylmethacralate (PMMA) has caused significant pollution, requiring advanced recycling methods. Pyrolysis, co‐pyrolysis, and catalytic co‐pyrolysis are key for recycling these wastes, necessitating kinetic studies and specific reactor designs of their thermal degradation. Isothermal thermogravimetric analysis at 300, 350, 400, 450, and 500°C was used to study thermal degradation kinetics, based on the non‐isothermal degradation zone of Bakelite. The batch pyrolysis of discarded Bakelite and PS/PMMA–Bakelite blends was conducted at 450°C. The thermal decomposition of Bakelite and its blends increases with higher isothermal pyrolytic temperatures. The addition of PS or PMMA to Bakelite substantially accelerates its thermal decomposition. The maximum weight loss of Bakelite, PS–Bakelite, and PMMA–Bakelite are 55%, 96.75%, and 89.51% at 500°C, respectively. The kinetic analysis is crucial for designing specific reactors, utilizing the D1‐diffusion‐based method for Bakelite, with an activation energy (Ea) of 17.178 kJ/mol and Arrhenius constant (A) of 0.095 min−1. The A2‐ and A3‐Avrami–Erofeyev methods explain the isothermal degradation of PS–Bakelite and PMMA–Bakelite blends, with activation energies of 9.031 and 12.59 kJ/mol, and Arrhenius constants of 0.056 and 0.075 min−1, respectively. The co‐pyrolysis of PS–Bakelite yields the highest condensable products (66.76%) and needs the longest reaction time (320 min). The Fourier transform Infrared (FTIR) and gas chromatography–mass spectrometry (GC–MS) analyses confirm the presence of alkanes, cycloalkanes, alkenes, cycloalkenes, aromatic hydrocarbons, and oxygenated compounds in the pyrolytic oils. This study provides unique kinetic parameters and product analyses, showing effects of blending on the decomposition rates and yields valuable compounds, advancing recycling technologies.
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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 | 0.001 |
| 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) | 0.002 | 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".