Synergistic effects of salt and silica nanoparticles on the properties of cellulose nanocrystal photonic films
Bibliographic record
Abstract
Cellulose nanocrystal (CNC) derived photonic materials hold significant potential for optical devices, multifunctional sensing and anticounterfeiting applications. However, these materials often exhibit limited color tunability and suffer from poor color stability under varying humidity conditions, restricting their broader functional potential. Herein, a multifunctional CNC-based photonic film was developed by introducing biobased silica nanoparticles (SiO 2 NP) and poly (vinyl alcohol) (PVA) into the CNC matrix. The ionic salt associated with SiO 2 NPs compressed the CNC helical pitch, resulting in a blue-shifted and tunable structural color in CNC-PVA composites. Further, small-angle X-ray scattering analysis revealed that incorporating SiO 2 NPs increased packing density and fractal complexity without large-scale aggregation, enabling tunable nanoscale organization. Subsequently, SiO 2 NPs reinforced the CNC-PVA composite photonic films by acting as nanofillers, enhancing both tensile strength and Young's modulus. The composite photonic films remarkably showed stable structural color up to 94 % relative humidity (RH) and reversible color change at 99 % RH. Utilizing this selective reversible responsive behavior, a multi-level encryption system was fabricated in which an apparent code is visible after the insertion of black background under ambient conditions, while the true information is revealed upon exposure to 99 % RH. This work demonstrates a sustainable approach to designing a CNC-based advanced optical device for high-security information storage and robust encryption-decryption technology. • SiO 2 NP and PVA tailored multiple functions of CNC photonic films. • Nanostructure assembly mechanism in CNC composites was elucidated. • SiO 2 NP acted as nanofiller to improve composite mechanical properties. • Films show stable color up to 94 % RH and reversible change at 99 % RH. • Humidity-responsive films enabled multi-level information encryption.
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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".