Aging Time of Soluble Potato Starch Solutions for Ultrafine Fibers Formation by Electrospinning
Bibliographic record
Abstract
The objective of this study is to develop a methodology to produce ultrafine fibers of soluble potato starch with normal amylose content by electrospinning, and to evaluate the characteristics of the fibers produced from fiber‐forming solutions subjected to different aging times. The fiber‐forming polymer solutions are prepared with 40% soluble potato starch (with amylose content of 32.54 ± 3.65%) and formic acid (75%) as solvent. The solutions are allowed to age for 0, 24, 48, and 72 h before electrospinning. Viscosity and electrical conductivity of the solutions are determined. The electrospun fibers are analyzed for morphology, size distribution, and thermal properties, and investigated through Fourier‐transform infrared spectroscopy analysis. The shear viscosity of the solutions decreases as aging time increases from 0 to 48 h but did not change upon further aging (p > 0.05). The electrical conductivity did not influenced the studied properties in the present study such as morphology and size distribution of the fibers. The electrospun fibers show a morphology with beads and average diameter in the range 128–143 nm. The weight loss in thermal properties is lower in the fibers than in the starch. This study showes that soluble potato starch with normal amylose content can be converted into ultrafine fibers by electrospinning like a neat polymer in a fiber‐forming solution.
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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".