MétaCan
Menu
Back to cohort
Record W4389107923 · doi:10.1002/tqem.22161

PET pyrolysis: Kinetics of a‐graphite

2023· article· en· W4389107923 on OpenAlex
Zinnia Chowdhury, Sanjib Barma, Dwaipayan Sen, Aparna Sarkar

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueEnvironmental Quality Management · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicRecycling and Waste Management Techniques
Canadian institutionsHeritage College
Fundersnot available
KeywordsPyrolysisIncinerationMaterials scienceActivation energyKineticsChemical engineeringMixed wasteWaste managementGraphiteMunicipal solid wasteHeat of combustionFourier transform infrared spectroscopyProcess engineeringCombustionRadioactive wasteOrganic chemistryChemistryComposite materialEngineering

Abstract

fetched live from OpenAlex

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 R 2 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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.509
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.002

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.

Opus teacher head0.016
GPT teacher head0.263
Teacher spread0.247 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it