Electrospinning of Technical Lignins for the Production of Fibrous Networks
Bibliographic record
Abstract
Electrospinning is an effective strategy to produce micron and sub-micron diameter fibrous networks from a variety of polymeric systems. Using seven different technical lignins the effect of lignin structure on fiber formation by electrospinning was studied. Surprisingly, none of the technical lignins could be electrospun into continuous fibers, although beaded fiber formation was observed for the softwood Kraft lignin system at high concentration (>50 wt%). However, the addition of poly(ethylene oxide) dramatically affected the electrospinning behavior and fiber formation. For all of the technical lignins a clear transition from electrospray or beaded fibers to uniform fibers was observed upon addition of poly(ethylene oxide); the lignin concentration dependent on poly(ethylene oxide) content. In all of the systems a linear increase in fiber diameter with increasing lignin concentration was observed. At the same concentration, the various lignin solutions had varying viscosities and different electrospinning behavior, that is, fiber diameter and ability to form uniform fibers, suggesting lignin specific structures and intermolecular interactions are influencing solution properties and electrospinning behavior. In fact, specific viscosity versus concentration plots reveal scaling exponents’, η ∼ c7.4–7.8 consistent with a branched polymer participating in intermolecular interactions such as hydrogen bonding or association complexes.
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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".