Holocellulose nanofibrils as effective nisin immobilization substrates for antimicrobial food packaging
Bibliographic record
Abstract
Holocellulose nanofibrils (HCNF), a type of nanocellulose with abundant amorphous regions suitable for chemical modification, show promise for sustainable food packaging but remain underutilized. This study employed HCNF to immobilize nisin and then spray-coated on the surface of soy protein isolate (SPI) films to improve mechanical, barrier, and antimicrobial properties. HCNF was extracted from wood sheet through chemical delignification and low-energy defibrillation, and then oxidized to introduce aldehyde groups for efficient nisin conjugation. The abundant amorphous regions led to a high immobilization rate of 3.4 mg/g, and conjugated HCNF and nisin coatings on SPI films significantly enhanced tensile strength to 3.43 ± 0.09 MPa, reduced water vapor permeability to 2.48 ± 0.07 × 10 −6 g m −1 h −1 Pa −1 , and decreased oxygen permeability to 4.29 ± 0.46 × 10 −4 cm 3 m −1 day −1 atm −1 . The conjugate sustained inhibition of S. aureus and L . monocytogenes , and the coating of 9 wt% conjugate on SPI films resulted in a 6-log reduction in bacterial count for both bacteria, while free nisin lost its antimicrobial efficacy during 24 h of pre-incubation. This work suggests feasibility of using HCNF as an effective substrate for nisin immobilization, providing a sustainable functional packaging solution with extended antimicrobial activity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 | 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 teacher head, 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".