From Structure to Properties of Composite Films Derived from Cellulose Nanocrystals
Bibliographic record
Abstract
Many natural materials exhibit a multilayer structure in which adjacent layers rotate in a helicoidal manner. The remarkable optical and mechanical properties of these materials have motivated research and development of man-made materials with similar morphology. Among them, composite materials by cellulose nanocrystals (CNCs) and polymers have attracted great interest; however, the relationship between the cholesteric structure and the material properties is not well understood. We used the composite CNC-polymer latex films with random, stratified, and cholesteric morphologies, all with the same compositions, to explore the effect of structure on the optical and mechanical properties of the composite films. Films with a cholesteric structure exhibited strong extinction, circular dichroism, and high stiffness; however, they had lower toughness than the films with the cholesteric stratified morphology. Films with disordered morphologies exhibited the highest toughness and the lowest stiffness. These trends were attributed to the confinement effects and the difference in polymer distribution in the films. These results provide guidance for the preparation of biomimetic cholesteric films with targeted optical and mechanical properties.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
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".