Controlling superstructure formation and macro-scale adhesion via confined evaporation of cellulose nanocrystals
Bibliographic record
Abstract
Abstract The self-assembly of cellulose nanocrystals can tether their interfacial interactions and the associated properties of their constructs. For instance, assemblies of highly aligned cellulose nanocrystals (CNCs) bear improved mechanical strength, barrier properties, and piezoelectric response. In this study, the self-assembly of CNC superstructure was assessed under various confinement geometries, enabling optimization of the long-range order within the microstructures formed. The confinement involved the planar capillary (with a rectangular cross-section)formed between two glass substrates with silicone boundaries. The impact of temperature, width and thickness of the capillary plane on self-assembly of the micro-scaled lamellar structures was evaluated. Thinner capillaries and lower temperatures were found to considerably improve long-range order and increase the frequency of the periodic microstructures formed. The drying process was monitored by rheological analysis, which showed an initial fast drying followed by slow drying due to the hindered diffusion through lamellae. The adhesive properties of the formed superstructures were evaluated. The shear strength was shown to depend on the orientation of the superstructures and therefore of the CNCs. About 4 MPa adhesion strength was obtained when the lamellar superstructures were perpendicularly aligned with respect to the in-plane force applied, while ca. 3 MPa adhesion was obtained for parallel alignment. The experimental framework described herein enabled the evaluation of the impact of the dimensions of a drying meniscus on self-assembly of anisometric colloids while also linking cellulosic assemblies with their interfacial supramolecular interactions. This simple framework brings forward the possibility to correlate the behavior of nanometric objects with micro- and macro-scaled observations, e.g., macro-scaled mechanics of adhesion.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".