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Record W4296071474 · doi:10.1016/j.mfglet.2022.07.046

Manufacturing of Pitch Based Carbon Fibers through Microwave Treatments

2022· article· en· W4296071474 on OpenAlexaff
Ge Lin, Talha Zafar, Danny Wong, Simon S. Park

Bibliographic record

VenueManufacturing Letters · 2022
Typearticle
Languageen
FieldEngineering
TopicFiber-reinforced polymer composites
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMaterials scienceCarbonizationComposite materialUltimate tensile strengthThermal treatmentFourier transform infrared spectroscopyModulusCarbon fibersMicrowaveMesophaseThermalSpecific modulusElastic modulusComposite numberChemical engineeringScanning electron microscope

Abstract

fetched live from OpenAlex

Carbon fibers (CFs) are characterized by their excellent mechanical properties including high tensile strength and elastic modulus. In terms of manufacturing of CFs, the conventional thermal treatment processes, including stabilization and carbonization, account for at least 46% of the cost, which drastically restricts the widespread application of CFs. This study investigates the use of mesophase pitch-based CFs from different synthetic approaches based on the conventional thermal post-treatment versus microwave hybrid post-treatment processes. CFs were studied by using SEM, FTIR, TGA, XRD, and mechanical properties characterization. Compared with the CFs prepared from the conventional thermal post-treatment process (CFs-T), those by the microwave thermal hybrid post-treatment process (CFs-M) show similar crystalline degree which is confirmed by XRD analysis. The tensile strength and modulus of the CFs-T are 0.290 and 38.6 GPa, and those of the CFs-M are 0.176 and 18.5 GPa, respectively. Microwave treatment is expected to be a much more energy- and time-efficient way for CFs post-treatment and can serve as a potential alternative to the conventional thermal post-treatment process used in carbon fiber manufacturing. Implications of the findings, including the oxygen diffusion, and crystalline structure are highlighted and discussed.

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 head0.007
GPT teacher head0.180
Teacher spread0.173 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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