Functionalized Lignin Derivatives as Melt‐Spinnable Precursors for Carbon Fiber Production without Stabilization
Bibliographic record
Abstract
Abstract Softwood kraft lignin, produced during industrial pulping processes, is investigated as a carbon fiber precursor with the distinct advantage of having smaller carbon footprint compared to polyacrylonitrile. Despite its advantageous high carbon content and aromatic structure for carbon fiber production, lignin presents challenges due to its brittleness and limited thermal processability. To overcome this intractability, a two‐step chemical modification method is applied to softwood kraft lignin, converting nearly all available hydroxy groups into cinnamate esters. NMR analysis revealed a high‐degree substitution of ester bonds, while FT‐IR showed significant reduction in hydroxyl stretching. The bulky aromatic groups allowed for stable melt spinning, while adding additional flexibility to the fiber, creating 100% lignin‐derivative fibers at an uptake speed of 50 m min −1 . Thermal and chemical oxidation methods are compared prior to carbonization. After carbonization to 1000 °C, both stabilization methods are equally effective. Spun samples are stretched under heat to achieve a diameter of 11 µm, which significantly enhanced the mechanical properties of the resulting carbon fiber. Upon carbonization, the resulting carbon fiber exhibited superior mechanical properties compared to most lignin‐based carbon fibers in the literature, reaching values over 120 GPa modulus and nearly 1 GPa strength.
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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".