Engineering nanocellulose for emerging dental material applications
Bibliographic record
Abstract
OBJECTIVES: This review examines recent advances in the use of nanocelluloses in dental materials, including cellulose nanocrystals (CNCs) and cellulose nanofibrils (CNFs); identifies property-function relationships; and highlights opportunities to broaden their application across dentistry. METHODS: A targeted literature search from 2000 to 2025 was conducted in Web of Science, Scopus, and PubMed including keywords related to nanocellulose and dental materials. Keywords "cellulose derivatives" or "bacterial cellulose" were excluded from the search. Additional sources were identified through citation screening of relevant papers. RESULTS: Nanocelluloses, particularly CNCs and CNFs, have been investigated for incorporation into some categories of dental materials such as dental composites and dental cements. In addition, their use as metallic surface coatings, drug delivery systems, remineralizing strategies and in tissue engineering scaffolds have been explored. Nanocelluloses are primarily applied as mechanical reinforcing agents, with optimal properties often achieved at low loadings. CNCs impart stiffness due to their crystallinity, while CNFs contribute toughness through fibrillar entanglement. However, the hydrophilic nature of nanocelluloses promotes aggregation, non-uniform dispersion, and poor compatibility with hydrophobic matrices, which remains a key challenge in application development. To address current bottlenecks, this review outlines future directions, including advanced nanocellulose surface functionalization strategies, leveraging aqueous processing for sustainability, and expanding nanocelluloses into multifunctional applications such as adhesives, 3D-printable resins, and bioactive composites. SIGNIFICANCE: This review provides a critical and forward-looking overview to establish a foundation for guiding and stimulating future research on integrating nanocelluloses into a diverse range of dental 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 | 0.000 | 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".