Electrospinning of aqueous lignin/poly(ethylene oxide) complexes
Bibliographic record
Abstract
ABSTRACT Microfibers of kraft lignin blended with poly(ethylene oxide) (PEO) were produced by electrospinning of the solution of lignin and high molecular weight poly(ethylene oxide) (PEO) in alkaline water. Interactions between lignin and PEO in alkaline aqueous solutions create association complexes, which increases the viscosity of the solution. The effect of polymer concentration, PEO molecular weight, and storage time of solution before spinning on the morphology of the fibers was studied. It showed that after one day the viscosity dropped and fiber diameter decreased. Results from the solutions in alkaline water and N,N‐dimethylformamide (DMF) with different polymer concentrations were compared. The 7 wt % of (Lignin/PEO: 95/5 wt/wt) in alkaline aqueous solution was successfully spun and the ratio of PEO in lignin/PEO mixture could be further reduced. In comparison, higher concentrations were needed to prepare a spinning solution in DMF and fiber diameters were in a much smaller range. The final target of spinning lignin is to produce carbonized fibers. Fibers spun from aqueous solutions had lower PEO content, which is a big advantage for the carbonization process as it reduces the challenges regarding melting of the fibers or void creation during carbonization. Furthermore, the larger diameter of these fibers inhibits disintegration of the carbonized fibers, which happens due to the mass loss during the process. © 2014 The Authors Journal of Applied Polymer Science Published by Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2015, 132, 41260.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".