Carbon Fibers: From PAN to Asphaltene Precursors; A State-of-Art Review
Bibliographic record
Abstract
Due to their outstanding material properties, carbon fibers are widely used in various industrial applications as functional or structural materials. This paper reviews the material properties and use of carbon fiber in various applications and industries and compares it with other existing fillers and reinforcing fibers. The review also examines the processing of carbon fibers and the main challenges in their fabrication. At present, two main precursors are primarily utilized to produce carbon fibers, i.e., polyacrylonitrile (PAN) and petroleum pitch. Each of these precursors makes carbon fibers with different properties. However, due to the costly and energy-intensive processes of carbon fiber production based on the existing precursors, there is an increasingly growing need to introduce cheaper precursors to compete with other fibers on the market. A special focus will be given to the most recent development of manufacturing more sustainable and cost-effective carbon fibers derived from petroleum asphaltenes. This review paper demonstrates that low-cost asphaltene-based carbon fibers can be a substitute for costly PAN/pitch-based carbon fibers at least for functional applications. The value proposition, performance/cost advantages, potential market, and market size as well as processing challenges and methods for overcoming these will be discussed.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".