Electrospinning of Softwood Organosolv Lignin without Polymer Addition
Bibliographic record
Abstract
Due to its nature and structure, lignin is a very difficult polymer to electrospin without any additives as only a few studies reported on the successful electrospinning of lignins in general and even more limited for softwood lignins to date. This paper highlights the possibility to use softwood organosolv lignin as a precursor for fiber electrospinning without any polymer addition. We have successfully electrospun pure organosolv softwood lignin into uniform, bead-free fibers. A concentration of 57 wt % of lignin in dimethyl formamide was determined as optimal, while other processing parameters (voltage, needle–collector distance, flow rate, and humidity) were studied to improve the fiber uniformity. We also studied the effects of minimum FeCl 3 addition and demonstrated its efficiency in improving the processability. Thus, the addition of 2 wt % FeCl 3 allowed decreasing the minimum fiber diameter from 400 to 200 nm. The addition of FeCl 3 also resolved the problem of fiber fusion on the collector, and it also allowed increasing the glass transition temperature of lignin fibers. These results open a way to new applications of lignin, such as 100% biosourced carbon fiber since no petroleum-sourced molecules were used in this study.
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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".