Understanding the self-assembly process of cellulose nanocrystals : towards chiral photonic materials
Bibliographic record
Abstract
Over millions of years, animals and plants have evolved complex molecules, macromolecules, and structures that endow them with vibrant colors. Among the sources of natural coloration, structural color arises from the interaction of light with nanoscale features rather than absorption from a pigment. Cellulose nanocrystals (CNCs) are materials derived from biorenewable resources that form a chiral nematic liquid crystal phase in water. Interestingly, the chiral nematic structures are maintained in solid films of CNCs obtained by drying the liquid crystalline phase. The self-assembly process of CNC building blocks is a complex topic that is not entirely understood; thus, further investigation of the self-assembly process will be crucial in developing tunable and colorful films. CNC films were prepared and the self-assembly process was investigated by varying the evaporation times for a drying CNC suspension. CNC films with reflected colors spanning the visible range were prepared by controlling the evaporation time, with the most blue-shifted reflection emerging from films formed with the slowest evaporation rates. An intermediate stage of self-assembly occurring before kinetic arrest helps explain the discrepancies in chiral nematic order of films prepared at different evaporation times. Local restriction of evaporation resulted in a patterned film with tunable optical properties. These thin film materials, although brittle, represent a system with localized control of the chiral nematic structures. The development of mechanically responsive photonic materials based on CNCs was made possible with the incorporation of elastomers. Elastomers containing a chiral nematic arrangement of CNCs inside were prepared and the resulting material showed reversible visible color upon mechanical stimulation. This material exhibits colors spanning the visible spectrum depending on the amount of stretching. CNCs are an exciting building block that can be applied to sustainable material development. To develop unique photonic materials for different applications, the intricacies of the self-assembly process must be further investigated. Specifically, the aim of this thesis is to study the chiral nematic organization of CNCs, starting from a suspension into a solid and colorful material. Photonic materials based on CNCs are attractive for applications in sensing and privacy but could also serve in decorations and coatings.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".