Modification of Nanocrystalline Cellulose for Bioactive Loaded Films
Bibliographic record
Abstract
Despite the use of petrochemical derived packaging, many problems such as browning and food spoilage still happen in food after harvesting. There is an increasing consumers concern for food shelf life to be extended as much as possible along with a big interest in green and bioactive materials, that could be used in direct contact with aliments. In order to reach public demand, biopolymers coming from natural sources such as plants or animals have been used to replace synthetic materials. Even though natural polymers are biodegradable, they do not reach regulations required with respect to mechanical properties in commercial applications. However, the mechanical properties can be improved when reinforced with nanoparticles. Several reinforcing nanoparticules such as clays, silica or silver have been used for industrial applications, but cellulose nanocrystals (CNCs) are a better choice for food industry due to their biodegradable and biocompatible nature as well as their outstanding potential in improving mechanical and barrier properties of nanocomposites. CNCs consist of anhydroglucopyranose units (AGU) linked together and several functional hydroxyl groups found on its surface. Modifications of the CNC surface chemistry can give to cellulose new functionalities that open the way to the development of new bioactive reinforcement in food packaging. The present review will be focused on covalent and non covalent modifications that can be achieved on surface CNC with the aim of adding functionalities to be applied for food 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) | 0.002 | 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".