Porous nanocellulose gels and foams: Breakthrough status in the development of scaffolds for tissue engineering
Bibliographic record
Abstract
We report on the latest scientific advances related to the use of porous foams and gels prepared with cellulose nanofibrils (CNF) and nanocrystals (CNC) as well as bacterial nanocellulose (BNC) – collectively nanocelluloses – as biomedical materials for application in tissue regeneration. Interest in such applications stems from the lightweight and strong structures that can be efficiently produced from these nanocelluloses. Dried nanocellulose foams and gels, including xerogels, cryogels, and aerogels have been synthesized effortlessly using green, scalable, and cost-effective techniques. Methods to control structural features (e.g., porosity, morphology, and mechanical performance) and biological interactions (e.g., biocompatibility and biodegradability) are discussed in light of specific tissues of interest. The state-of-the-art in the field of nanocellulose-based scaffolds for tissue engineering is presented, covering physicochemical and biological properties relevant to these porous systems that promise groundbreaking advances. Specifically, these materials show excellent performance for in vitro cell culturing and in vivo implantation. We report on recent efforts related to BNC scaffolds used in animal and human implants, which furthermore support the viability of CNF- and CNC-based scaffolds in next-generation biomedical materials.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".