Effect of fiber microstructure studied by Raman spectroscopy upon the mechanical properties of carbon fibers
Bibliographic record
Abstract
Abstract The microstructure of polyacrylonitrile (PAN)‐based high‐performance carbon fibers, including high‐strength carbon fibers (HSCFs), high‐modulus carbon fibers (HMCFs), and ultrahigh‐modulus carbon fibers (UHMCFs), was systematically characterized by the Raman spectroscopy. Two characteristic bands, D‐line and G‐line, showed up in the Raman spectra of HSCFs, HMCFs, and UHMCFs. However, the wavenumber of the G‐line peak of HSCFs shifted to higher wavenumber (about 1,595 cm−1) and that of the D‐line peak of HMCFs and UHMCFs shifted to lower wavenumber (about 1,350 cm−1). The relationship between the microstructure and mechanical properties of carbon fibers was also studied in detail. It was of significant relevance between surface disordered structure and the mechanical properties of HSCFs, and decreases in the full width at half maximum values of the disorder‐induced D‐line and A‐line could result in higher tensile strength and tensile modulus of HSCFs. As for HMCFs and UHMCFs, the disorder‐induced D′‐line more easily affected the tensile strength. A higher tensile modulus of HMCF and UHMCF was obtained as a result of decreases in the disordered structure and increases in the graphite structure. An increase of the intensity ratio ID/IG together with IA/IG (HSCFs) or ID′/IG (HMCFs and UHMCFs) could result in increases in the tensile strength and tensile modulus of carbon fibers.
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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) | 0.000 | 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".