A Review on Surface-Functionalized Cellulosic Nanostructures as Biocompatible Antibacterial Materials
Bibliographic record
Abstract
As the most abundant biopolymer on the earth, cellulose has recently gained significant attention in the development of antibacterial biomaterials. Biodegradability, renewability, strong mechanical properties, tunable aspect ratio, and low density offer tremendous possibilities for the use of cellulose in various fields. Owing to the high number of reactive groups (i.e., hydroxyl groups) on the cellulose surface, it can be readily functionalized with various functional groups, such as aldehydes, carboxylic acids, and amines, leading to diverse properties. In addition, the ease of surface modification of cellulose expands the range of compounds which can be grafted onto its structure, such as proteins, polymers, metal nanoparticles, and antibiotics. There are many studies in which cellulose nano-/microfibrils and nanocrystals are used as a support for antibacterial agents. However, little is known about the relationship between cellulose chemical surface modification and its antibacterial activity or biocompatibility. In this study, we have summarized various techniques for surface modifications of cellulose nanostructures and its derivatives along with their antibacterial and biocompatibility behavior to develop non-leaching and durable antibacterial materials. Despite the high effectiveness of surface-modified cellulosic antibacterial materials, more studies on their mechanism of action, the relationship between their properties and their effectivity, and more in vivo studies are required.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".