Using carboxylated cellulose nanofibers to enhance mechanical and barrier properties of collagen fiber film by electrostatic interaction
Bibliographic record
Abstract
Abstract BACKGROUND Collagen‐based films including casings with a promising application in meat industry are still needed to improve its inferior performance. In the present study, the reinforcement of carboxylated cellulose nanofibers (CNF) for collagen film, based on inter‐/intra‐ molecular electrostatic interaction between cationic acid‐swollen collagen fiber and anionic carboxylated CNF, was investigated. RESULTS Adding CNF decreased the zeta‐potential but increased particle size of collagen fiber suspension, with little effect on pH. Furthermore, CNF addition led to a higher tensile strength but a lower elongation, and the water vapor and oxygen barrier properties were improved remarkably. Because the CNF content was 50 g kg–1 or lower, the films had a homogeneous interwoven network, and CNF homogeneously embedded into collagen fiber matrix according to the scanning electron microscopy and atomic force microscopy analysis. Additionally, CNF addition increased film thickness and opacity, as well as swelling rate. CONCLUSION The incorporation of CNF endows collagen fiber films good mechanical and barrier properties over a proper concentration range (≤ 50 g kg–1 collagen fiber), which is closely associated with electrostatic reaction of collagen fiber and CNF and, subsequently, the form of the homogenous, compatible spatial network, indicating a potential applications of CNF in collagenous protein films, such as edible casings. © 2017 Society of Chemical Industry
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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".