Cellulose Nanowhiskers and Fiber Alignment Greatly Improve Mechanical Properties of Electrospun Prolamin Protein Fibers
Bibliographic record
Abstract
Electrospun fibers from natural polymers must possess appropriate mechanical properties if they are to be functional in numerous applications. In this research, two convenient physical approaches were applied to reinforce the assembled hordein/zein electrospun nanofabrics: incorporation of surface-modified cellulose nanowhiskers (SCN) and fiber alignment. The mechanical properties and stability of the modified fibers were tested in relation to fiber morphology and structure as characterized by scanning electron microscopy, transmission electron microscopy, Fourier-transform infrared spectroscopy, and Raman spectroscopy. SCN modified by quaternary ammonium salt were well-dispersed in hordein/zein networks, leading to fibers with significantly improved mechanical properties and water resistance. With the addition of 3 wt % SCN, the tensile strength and Young's modulus of hordein/zein fibers increased from 4.36 ± 0.29 to 7.79 ± 0.36 MPa and from 195.80 ± 13.02 to 396.64 ± 18.33 MPa, respectively, and the elongation at break was retained because of the formation of a percolating network of SCN. The alignment of electrospun fibers strengthened the hordein/zein nanofabrics in both tangential and normal directions to 17.26 ± 1.41 and 14.02 ± 0.74 MPa, respectively, by not only altering the piling up pattern, but also by promoting phase separation and improved interactions. When applying both of the reinforcing methods, the tensile strength of hordein/zein fibers was further enhanced to 21.99 ± 1.19 MPa, stronger than that of cancellous bones (5-10 MPa). All the reinforced fibers exhibited a reduced burst effect in phosphate-buffered saline (PBS) while releasing the incorporated bioactive molecule in a controlled manner. These physically reinforced prolamin protein fibers possessed significantly improved mechanical properties and may have potential to be used as tissue engineering scaffold materials or natural delivery systems for biomedical applications.
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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) | 0.000 | 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".