Computational study of the texture formation in mesophase pitch‐based carbon fibres
Bibliographic record
Abstract
This paper studies the thermal relaxation phenomena after melt‐extrusion of a rigid discotic uniaxial nematic mesophase pitch using mathematical modelling and computer simulation. The Ericksen and Landau–de Gennes continuum theories are used to investigate the structure development and texture formation across mesophase pitch‐based carbon fibres. The two‐dimensional model captures five types of transverse patterns, which match the commonly observed textures for mesophase pitch‐based carbon fibres. They are: random, zig‐zagged radial, radial, quasi‐onion and onion. These textures represent the various combinations possible from the interplay between structure (i.e. texture) development and cooling during the fibre spinning process. During the thermal relaxation after the cessation of extensional flow the discotic nematic molecules store elastic free energy decays. The distorted nematic molecular profiles reorient to release the stored elastic free energy. The difference in time scales for molecular reorientation and thermal relaxation result in different transverse textures. The rate at which the fibres are cooled is the main factor in controlling the structure development. A slow cooling rate would permit the nematic discotic molecules to reorient to a well‐developed (radial or onion) texture. The random texture is a result of rapid quenching. The numerical results are consistent with published experimental observations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| 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".