Effect of temperature and holding time on preoxidation for aligned electrospun polyacrylonitrile nanofibers
Bibliographic record
Abstract
ABSTRACT In this article, aligned electrospun polyacrylonitrile nanofiber bundles were prepared as the precursor fibers to prepare preoxidized nanofibers through washing, drying densification, damp‐heat drafting, and preoxidation process. Effects of preoxidation temperature and holding time on appearance and microstructure of the preoxidized fibers were studied. Fiber density is increased from 1.159 to 1.193 g cm−3 after drying densification. Crystallinity is increased from 22.66 to 45.90% after fourfold drafting. The aligned preoxidized nanofibers were prepared at the optimum preoxidation temperature of 283°C, heating speed of 1°C min−1, and holding time of 1 h show a sufficient reaction degree of cyclization and crosslinking. Moreover, there is no occurrence of adhesion between fibers. © 2013 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 130: 1158‐1163, 2013
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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 | 0.001 |
| 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".