Iodine Treatment of Lignin–Cellulose Acetate Electrospun Fibers: Enhancement of Green Fiber Carbonization
Bibliographic record
Abstract
Abstract Pure biopolymer-based electrospun precursor carbon fibers are fabricated using an abundant and inexpensive biopolymer lignin blended with renewable resource-based cellulose acetate (CA). Iodine treatment on the fabricated green fiber was successfully performed in order to enhance the carbonization process as well as the retention of fiber morphology. The absorption mechanism of iodine by lignin and cellulose acetate and their derived electrospun green fibers has been investigated by means of thermal behavior and morphological retention. It was found that iodine treatment plays a vital role in altering the graphitization behavior as well as morphology retention during the carbonization process. With the help of iodine treatment, the green precursor fibers were successfully converted into thin carbon fibers, and scanning electron microscopy analysis confirmed the retention of fibrous structures with diameters around 250 nm. Raman spectroscopy revealed that although the overall level of graphitization was lower compared to polyacrylonitrile-based fibers, the graphitic crystallite size was larger in the produced carbon fibers. The produced pure biopolymer fibers and iodine treatments show promise for the production of green and cost-reduced 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 | 0.000 |
| 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".