Functional Materials from Nanocellulose: Utilizing Structure–Property Relationships in Bottom‐Up Fabrication
Bibliographic record
Abstract
It is inherently challenging to recapitulate the precise hierarchical architectures found throughout nature (such as in wood, antler, bone, and silk) using synthetic bottom-up fabrication strategies. However, as a renewable and naturally sourced nanoscale building block, nanocellulose-both cellulose nanocrystals and cellulose nanofibrils-has gained significant research interest within this area. Altogether, the intrinsic shape anisotropy, surface charge/chemistry, and mechanical/rheological properties are some of the critical material properties leading to advanced structure-based functionality within nanocellulose-based bottom-up fabricated materials. Herein, the organization of nanocellulose into biomimetic-aligned, porous, and fibrous materials through a variety of fabrication techniques is presented. Moreover, sophisticated material structuring arising from both the alignment of nanocellulose and via specific process-induced methods is covered. In particular, design rules based on the underlying fundamental properties of nanocellulose are established and discussed as related to their influence on material assembly and resulting structure/function. Finally, key advancements and critical challenges within the field are highlighted, paving the way for the fabrication of truly advanced materials from nanocellulose.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".