Coassembly of Gold Nanoparticles and Cellulose Nanocrystals in Composite Films
Bibliographic record
Abstract
Coassembly of nanoparticles with different size-, shape-, and composition-dependent properties is a promising approach to the design and fabrication of functional materials and devices. This paper reports the results of a detailed investigation of the formation and properties of free-stranding composite films formed by the coassembly of cellulose nanocrystals and shape-isotropic plasmonic gold nanoparticles. The effect of gold nanoparticle size, surface charge, and concentration on the structural and optical properties of the composite films has been studied. The composite films retained photonic crystal and chiroptical activity properties. The size and surface charge of gold nanoparticles had a minor effect on the structure and properties of the composite films, while the concentration of gold nanoparticles in the composite material played a more significant role and can be used to fine-tune the optical properties of materials derived from cellulose nanocrystals. These findings significantly broaden the range of nanoparticles that can be used for producing nanocomposite materials based on cellulose nanocrystals. The simplicity of film preparation, the abundance of cellulose nanocrystals, and the robust, free-standing nature of the composite films offer highly advantageous features and pave the way for the generation of functional materials with coupled optical properties.
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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".