Skipping Oxidative Thermal Stabilization for Lignin-Based Carbon Nanofibers
Bibliographic record
Abstract
Oxidative thermal stabilization is considered a critical process before carbonization to prevent fusion of fibers, while aiding in the formation of homogeneous fiber cross sections during carbon fiber manufacturing. In this study, we investigated the impact of nanocrystalline cellulose (NCC) on the thermal, electrical, and mechanical properties of electrospun lignin-derived carbon nanofibers when the oxidative thermal stabilization step was skipped. Results showed that by adding small amounts of NCC (up to 5 wt %), uniform lignin-based carbon nanofibers were prepared with direct carbonization processes without oxidative thermal stabilization. SEM images revealed that NCC filled lignin carbon nanofibers retained their fibrous morphology after the heat treatment, dependent upon the carbonization rate. Further, carbonization conditions were exploited to form a unique interconnected structure, which increased the electrical conductivity of carbon nanofiber mats from 5 to 35 S/cm. Dynamic thermomechanical analysis of NCC/lignin nanofiber mats showed a reduction of the tan δ peak during the glass transition indicating NCC restricted the molecular mobility of lignin’s chains. Through thermal rheological evidence, this study revealed significant interaction of NCC and lignin blends that prevented the fusion of nanofibers during heat treatment. This study is unique that it provides a method to reduce processing time and energy cost associated with carbon fiber production, while controlling fiber mat structure.
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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".