Effects of poly(ethylene oxide) and pH on the electrospinning of whey protein isolate
Bibliographic record
Abstract
Abstract Poly(ethylene oxide) (PEO) is known for facilitating the electrospinning of biopolymer solutions, which are otherwise not electrospinnable. The objective of this study was to improve the understanding of the positive effects of PEO on the electrospinning of whey protein isolate (WPI) solutions under different pH conditions. Alterations in protein secondary structure and polymer solution properties (viscosity, conductivity, and dynamic surface tension), as induced by pH changes, significantly affected the electrospinning behavior of WPI/PEO (10% w/w: 0.4% w/w PEO) solutions. Acidic solutions resulted in smooth fibers (707 ± 105 nm) while neutral solutions produced spheres (2.0 ± 1.0 μm) linked with ultrafine fibers (138 ± 32 nm). In comparison, alkaline solutions produced fibers (191 ± 36 nm) that were embedded with spindle‐like beads (1.0 ± 0.5 μm). 13C NMR and FTIR spectroscopies showed that the increase in random coil and α‐helix secondary structures in WPI were the main contributors to the formation of bead‐less electrospun fibers. The electrospinning‐enabling properties of PEO on aqueous WPI solutions were attributed to physical chain entanglement between the two polymers, rather than specific polymer–polymer interactions. © 2012 Wiley Periodicals, Inc. J Polym Sci Part B: Polym Phys, 2012
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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 | 0.001 |
| 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".