Concentric chiral nematic polymeric fibers from cellulose nanocrystals
Bibliographic record
Abstract
Hierarchical biological materials, such as osteons and plant cell walls, are complex structures that are difficult to mimic. Here, we combine liquid crystal systems and polymerization techniques within confined systems to develop complex structures. A single-domain concentric chiral nematic polymeric fiber was obtained by confining cellulose nanocrystals (CNCs) and hydroxyethyl acrylate inside a capillary tube followed by UV-initiated polymerization. The concentric chiral nematic structure continues uniformly throughout the length of the fiber. The pitch of the chiral nematic structure could be controlled by changing the CNC concentration. We tracked the formation of the concentric structure over time and under different conditions with variation of the tube orientation, CNC concentration, CNC type, and capillary tube size. We show that the inner radius of the capillary tube is important and a single-domain structure was only obtained inside small-diameter tubes. At low CNC concentration, the concentric chiral nematic structure did not completely cover the cross-section of the fiber. The highly ordered structure was studied using imaging techniques and X-ray diffraction, and the mechanical properties and structure of the chiral nematic fiber were compared to a pseudo-nematic fiber. CNC polymeric fibers could become a platform for many applications from photonics to complex hierarchical 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) | 0.000 | 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".