Biopolymer Templated Glass with a Twist: Controlling the Chirality, Porosity, and Photonic Properties of Silica with Cellulose Nanocrystals
Bibliographic record
Abstract
A detailed investigation of the formation and properties of mesoporous silica templated by the chiral nematic liquid crystal phase of cellulose nanocrystals (CNCs) is presented. Under appropriate conditions, CNCs co‐assemble with silica up to loadings of ≈60 wt% to give composite films with periodic chiral nanostructures. The periodicity of these films can be readily controlled to obtain materials that selectively reflect light with wavelengths ranging from ≈400–1400 nm. The co‐assembly of CNCs and silica into ordered chiral nematic structures is demonstrated to occur within a narrow window of pH and is affected by aging: a slow rate of silica condensation appears to be vital for the formation of well‐ordered materials. CNCs can be removed from the composite films by calcination or acid hydrolysis to give high surface area chiral nematic mesoporous silica (CNMS) with tunable pore diameters. The combination of mesoporosity and chiral nematic ordering in CNMS enables it to be used in a unique way for refractometric sensing applications. It is shown that, when using circular dichroism (CD) signals to monitor the chiral photonic properties of CNMS, variations in refractive index can be detected based on changes of both CD signal intensity and peak position with good sensitivity.
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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".