Shape‐Memory Photonic Thermoplastics from Cellulose Nanocrystals
Bibliographic record
Abstract
Abstract Responsive materials prepared using shape‐memory photonic crystals have potential applications in rewritable photonic devices, security features, and optical coatings. By embedding chiral nematic cellulose nanocrystals (CNCs) in a polyacrylate matrix, a shape‐memory photonic crystal thermoplastic (CNC‐SMP) allows reversible capture of different colored states is reported. In this system, the temperature is used to program the shape‐memory response, while pressure is used to compress the helical pitch of the CNC chiral nematic organization. By increasing the force applied (≈140–230 N), the structural color can be tuned from red to blue. Then, on‐demand, the CNC‐SMP can recover to its original state by heating it above the glass transition temperature. This cycle can be performed over 15 times without any loss of the shape‐memory behavior or mechanical degradation of the sample. In addition, multicolor readouts can be programmed into the chiral nematic CNC‐SMP by using a patterned substrate to press the sample, while the glass transition temperature of the CNC‐SMP can be tuned over a 90 °C range by altering the monomer composition used to prepare the polyacrylate matrix.
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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 | 0.000 |
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".