Continuously superior-strong carbon nanofibers by additive nanostructuring and carbonization of polyacrylonitrile jetting
Bibliographic record
Abstract
, but have to confront the challenge of maximizing the role of scale effects. Here, a method of additive nanostructuring and carbonization of polyacrylonitrile (PAN) jetting for the nano-forming of carbon fibers is developed by understanding the electrostatic submicro-initiation of a PAN jetting, altering the microstructure of PAN-based jetting fibers at the nanoscale and implementing subsequent carbonization of PAN jetting nanofiber. Using this method of additive nanostructuring and carbonization in combination with the radial distribution pattern of shear stress, we find that the conformation of some molecular chains inside the PAN nanofibers is transformed into the zigzag conformation. The ability to materialize and carbonize such PAN nanofibers with various conformational structures in the form of arrays on diverse micro-structures and macro-substrates enables the forming of continuous carbon nanofibers with a diameter of ~20 nm and allows the tensile strength of carbon fibers to be enhanced to 212 GPa through the combination of zigzag conformation and nanoscale effects. These advantages create opportunities for the application of maximizing nanoscale effects that have not previously been technically possible.
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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) | 0.000 | 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".