Electrospinning with Condensed Tannins: Effects on Co-spinning with Zein
Bibliographic record
Abstract
Electrospinning can be applied to renewables creating new materials and applications. We have utilized electrospinning in an attempt to create nanofibers from condensed tannins as well as from binary conjugates formed between this tannin with zein protein. While attempts to directly electrospin pine bark tannin extract proved unsuccessful, combining zein with this tannin gave electrospun fiber from acetic acid and dimethyl formamide (DMF) solutions. To achieve nanofibers possessing significant tannin content, high solids content (≥35%) in DMF solution was required. Electrospun nanofibers (200–400 nm dia.) could be created from zein-tannin combinations with up to 80% tannin content and appropriate solution solids content. Nanofibers could be produced from pre-formed tannin-zein conjugates or via their direct combination as a mixture in the spinning solution. Analysis of thermal stability shows the zein-tannin conjugates have similar thermal properties and stability to zein, being stable up to 240°C.
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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".