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Record W4410160192 · doi:10.3390/fib13050058

A Study on the Effect of an Oxidizing Atmosphere During the Recycling of CFRP by Pyrolysis

2025· article· en· W4410160192 on OpenAlexafffund
Cynthie Dega, Ali Jadidinia, Rachid Boukhili

Bibliographic record

VenueFibers · 2025
Typearticle
Languageen
FieldEngineering
TopicFiber-reinforced polymer composites
Canadian institutionsPolytechnique MontréalCTT Group (Canada)
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsOxidizing agentAtmosphere (unit)PyrolysisMaterials scienceReducing atmosphereChemical engineeringWaste managementMetallurgyChemistryOrganic chemistryEngineeringMeteorology

Abstract

fetched live from OpenAlex

Composite materials are increasingly in demand. However, challenges such as high raw-material costs and complicated waste management impede their adoption. Overcoming these obstacles requires efficient recycling methods. Pyrolysis effectively recycles carbon fiber-reinforced polymers (CFRPs). This study proposes a cost-effective CFRP recovery approach utilizing conventional ovens to minimize recycling expenses and maximize reclaimed-product value. Pyrolysis was conducted under atmospheric conditions at 450–600 °C, lasting 1–6 h at each temperature. It was optimal at 2.5 h and 500 °C. Higher temperatures caused fiber degradation, and lower temperatures excessively prolonged duration. After determining the optimal conditions, composite plates were produced using recycled carbon fibers and a vacuum-assisted resin infusion process. Subsequent physical characterization and mechanical tests were conducted on these plates to assess the recycled-CFRP properties. The recovered tensile strength and tensile modulus were 88% and 97% that of virgin carbon fibers (vCF), respectively.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.003
GPT teacher head0.211
Teacher spread0.208 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2025
Admission routes2
Has abstractyes

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