Optimization of Curing Process for Carbon Fiberpreparation from Wood-Phenol Liquefaction Product
Bibliographic record
Abstract
In this study, China fir was liquefied in phenol, and liquefactionproduct was used to produce carbon fiber precursors by curing process. The effect of heating rate, curing temperature, curing time and hydrochloric acid concentration on curing processwas investigated by orthogonal experimentsin term of the crystallinityof carbon fiber precursors produced.According to experiment results, the primary and secondary relation of the four variables is: curing time>curing temperature>hydrochloric acid concentration >heating rate. The optimal conditions of curing technology are as follow: heating rate of 15 °C/h, curing temperature of 90 °C, curing time of 2 h with hydrochloric acid concentration of 18.5%. Using the optimal conditions, carbon fiber precursors could obtain the highest crystallinity of 36.96%. Key words: Carbon fiber precursors; Curing; Crystallinity; Liquefaction; Phenol
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".