Effect of Iron Chloride Addition on Softwood Lignin Nano-Fiber Stabilization and Carbonization
Bibliographic record
Abstract
This study presents the effect of iron chloride addition on the production of nanocarbon fibers from softwood Organosolv lignin. It was shown that adding 2% FeCl3 to the lignin solution before electrospinning to produce lignin nanofibers increased the thermal resistance of lignin fibers during stabilization. FTIR and XPS analyses of the lignin fibers stabilized with and without FeCl3 revealed that the temperature rate could be increased in the presence of FeCl3 from 1 to 3 °C/min. The optimal temperature to stabilize the lignin fibers was found to be 250 °C, as higher temperatures led to thermal degradation. Also, carbon fibers were successfully produced from pure softwood Organosolv lignin fibers. Carbonization tests were conducted under nitrogen and the best parameters were determined to be a ramp of 10 °C/min until 600 °C with a holding time of 2 h. Furthermore, the effect of 2% FeCl3 addition in the lignin solution was investigated during these processes. XPS analysis showed a 93% carbon content for fibers carbonized with and without FeCl3 addition, while SEM images revealed some surface roughness in fibers with FeCl3 after carbonization. These results confirm that FeCl3 addition influences the carbon nanofiber production.
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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) | 0.001 | 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".