Responsive Chiral Photonic Cellulose Nanocrystal Materials
Bibliographic record
Abstract
Abstract Responsive photonic crystals have attracted significant attention in fundamental scientific research and technological applications. Upon applying external stimuli, such as mechanical, electrical, magnetic, and optical triggers, the optical properties of photonic crystals can be actively tuned. Among a large number of photonic crystals, cellulose nanocrystals (CNCs) are considered one of the most promising materials due to their renewability, simplicity of preparation, and unprecedented chirality. A comprehensive overview of the fundamental design principles, methodologies, responsive mechanisms, and practical applications of responsive chiral photonic CNC materials is presented here. The changes in helix pitch, refractive index, orientation, and resulting structural and optical properties of chiral photonic CNCs in response to various stimuli are discussed. Thereafter, novel applications of responsive chiral photonic CNCs, such as colorimetric sensors, photonic actuators, rewritable photonic papers, and anti‐counterfeiting smart tags are described. We conclude by discussing future challenges and opportunities for developing high‐performance responsive chiral photonic CNC 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 | 0.000 | 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".