Carbon Fibers with High Electrical Conductivity: Laser Irradiation of Mesophase Pitch Filaments Obtains High Graphitization Degree
Bibliographic record
Abstract
Carbon fibers have promising applications in the efficient transmission of electric power with less resource consumption. Laser graphitization of the mesophase pitch-based carbon fiber (MPCF) was proposed to improve the electrical conductivity of the carbon fiber. The obtained MPCF showed a high conductivity of 7.04 × 10 5 S/m. The graphitization degree of carbon fiber increased and the interlayer spacing of its graphite crystal decreased on increasing the laser intensity. With a laser power of 360 W and an irradiation period of 25 s, corresponding to a temperature of 3001 °C, a high graphitization degree ( R = 0.02) was obtained and the interlayer spacing was as small as 0.338 nm, close to that of pure graphitic carbon (0.335 nm). The enhanced electrical conductivity is attributed to the improved homogeneity of graphitization along the axial and radial directions, the ordered structural evolution, ordered stacking of the graphitic layer, and the promoted connectivity between the graphitic crystals in the carbon fiber. The energy consumption of the proposed method is estimated to be only 0.46% of that with the conventional Joule heating approach. This work suggests that laser-induced graphitization is a good alternative to prepare carbon fibers with high electrical conductivity and reduced energy consumption.
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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".