Structural Variations in Hybrid All-Nanoparticle Gibbsite Nanoplatelet/Cellulose Nanocrystal Multilayered Films
Bibliographic record
Abstract
Cellulose nanocrystals (CNCs) are promising biosourced building blocks for the production of high performance materials. In the last ten years, CNCs have been used in conjunction with polymers for the design of multilayered thin films via the layer-by-layer assembly technique. Herein, polymer chains have been replaced with positively charged inorganic gibbsite nanoplatelets (GN) to form hybrid "nanoparticle-only" composite films. A combination of atomic force microscopy and neutron reflectivity experiments was exploited to investigate the growth and structure of the films. Data show that the growth and density of GN/CNC films can be tuned over a wide range during preparation by varying the ionic strength in the CNC suspension and the film drying protocol. Specifically, thin and dense multilayered films or very thick, more porous mixed slabs, as well as intermediate internal structures, could be obtained in a predictable manner. The influence of key physicochemical parameters on the multilayer film buildup was elucidated and the film architecture was linked to the dominating interaction forces between the components. The degree of structural control over these hybrid nanoparticle-only films is much higher than that reported for CNC/polymer films, which offers new properties and potential applications as separation membranes or flame retardant coatings.
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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".