Chiral nematic cellulose nanocrystal composites: An organized review
Bibliographic record
Abstract
Abstract Cellulose nanocrystals (CNCs) are commercially available materials derived from cellulose, the most abundant biopolymer on our planet. Due largely to their high strength, high surface area‐to‐volume ratio, tailorable surface chemistry, and the abundance of biomass feedstocks with which to produce them, CNCs have attracted significant interest in applications spanning the paints and coatings, composites, packaging, and biomedical sectors. However, and perhaps most interestingly, CNCs will self‐assemble (or, as I've teased in the title, organize ) to form highly ordered chiral nematic liquid crystal phases when concentrated in suspension. Upon complete solvent evaporation, this chiral nematic order is ‘locked’, yielding films with structural colour—colour arising not due to chemical pigments, but rather due to the physical structure of a material itself. In the pursuit of novel multi‐functional materials, research interest has shifted recently towards the incorporation of functional additives to form composite chiral nematic films. Along with introducing the basics of liquid crystals and self‐assembly, this review discusses the main approaches used in order to form CNC‐based composite films: co‐assembly, templating, and post‐processing, and highlights exceptional examples in each case. Finally, I give my uniquely Canadian perspective on the current status, future prospects, and major challenges associated with the development of CNC‐based chiral nematic composite 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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".