Surface Modification of Cellulose Nanocrystals for Nanocomposites
Bibliographic record
Abstract
Cellulose nanocrystal is a promising reinforcing filler derived from biomass resources, but its application is limited due to poor dispersibility in organic solvents (as the blending media), lower thermal stability that is mismatched with the processing temperature, poor miscibility with the hydrophobic polymer matrix and resultant self-aggregation, and so on. Consequently, surface modification of cellulose nanocrystals has been explored in order to overcome/alleviate these problems. In this chapter, the advantages of surface modification of cellulose nanocrystals for manufacturing nanocomposites are reviewed. Methodologies, including physical attachment, coating, encapsulation, TEMPO oxidation, small molecule conjugation, and grafting strategies based on “graft onto” and “graft from” are presented comprehensively. Based on these methods/strategies, the surface-modified cellulose nanocrystals show high dispersibility in the blending media and enhanced thermal stability during melt compounding. In addition, miscibility between surface-modified cellulose nanocrystals and the polymeric matrix is improved, and a co-continuous structure mediated by the entanglements between the grafted chains and matrix is likely to be formed. Ultimately, surface modification of cellulose nanocrystals enhances the mechanical performance of resultant nanocomposites.
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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 | 0.000 | 0.000 |
| 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".