Retrieving the Coassembly Pathway of Composite Cellulose Nanocrystal Photonic Films from their Angular Optical Response
Bibliographic record
Abstract
Aqueous suspensions of cellulose nanocrystals (CNCs) are known to self-assemble into a chiral nematic liquid crystalline phase, leading to solid-state nanostructured colored films upon solvent evaporation, even in the presence of templating agents. The angular optical response of these structures, and therefore their visual appearance, are completely determined by the spatial arrangement of the CNCs when the drying suspension undergoes a transition from a flowing and liquid crystalline state to a kinetically arrested state. Here, it is demonstrated how the angular response of the final film allows for retrieval of key physical properties and the chemical composition of the suspension at the onset of the kinetic arrest, thus capturing a snapshot of the past. To illustrate this methodology, a dynamically evolving sol-gel coassembly process is investigated by adding various amounts of organosilica precursor, namely, 1,2-bis(trimethoxysilyl)ethane. The influence of organosilica condensation on the kinetic arrest can be tracked and thus explains the angular response of the resulting films. The a posteriori and in situ approach is general; it can be applied to a variety of additives in CNC-based films and it allows access to key rheological information of the suspension without using any dedicated rheological technique.
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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) | 0.001 | 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".