Review of Hydrogels and Aerogels Containing Nanocellulose
Bibliographic record
Abstract
Naturally derived cellulose nanocrystals (CNCs) and cellulose nanofibrils (CNFs) are emerging nanomaterials that display high strength, high surface area, and tunable surface chemistry, allowing for controlled interactions with polymers, nanoparticles, small molecules, and biological materials. Industrial production of nanocelluloses is increasing rapidly with several companies already producing on the tons-per-day scale, intensifying the quest for viable products across many sectors. While the hydrophilicity of the nanocellulose interface has posed a challenge to the use of CNCs and CNFs as reinforcing agents in conventional plastics, it is a significant benefit for creating reinforced or structured hydrogel composites (or, when dried, aerogels) exhibiting both mechanical reinforcement and a host of other desirable properties. In this context, this Review describes the quickly growing field of hydrogels and aerogels incorporating nanocelluloses; over 200 references are summarized in comprehensive tables covering the chemistry, preparation, properties, and applications of "nanocellulose-only" and "nanocellulose-containing" gels. Physical and chemical cross-linking strategies, postmodification steps, and routes to control gel structure are discussed, along with key developments and ongoing challenges in the field. Nanocellulose hydrogels and aerogels show great promise in a wide range of biomedical, energy storage, construction, separations, cosmetic, and food applications.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".