Exploiting Supramolecular Interactions from Polymeric Colloids for Strong Anisotropic Adhesion between Solid Surfaces
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Adhesion occurs by covalent bonding, as in reactive structural adhesives, or through noncovalent interactions, which are nearly ubiquitous in nature. A classic example of the latter is gecko feet, where hierarchical features enhance friction across the contact area. Biomimicry of such structured adhesion is regularly achieved by top‐down lithography, which allows for direction‐dependent detachment. However, bottom‐up approaches remain elusive given the scarcity of building blocks that yield strong, cohesive, self‐assembly across multiple length scales. Herein, an exception is introduced, namely, aqueous dispersions of cellulose nanocrystals (CNCs) that form superstructured, adherent layers between solid surfaces upon confined evaporation‐induced self‐assembly (C‐EISA). The inherently strong CNCs ( E A > 140 GPa) align into rigid, nematically ordered lamellae across multiple length scales as a result of the stresses associated with confined evaporation. This long‐range order produces remarkable anisotropic adhesive strength when comparing in‐plane (≈7 MPa) and out‐of‐plane (≤0.08 MPa) directions. These adhesive attributes, resulting from self‐assembly, substantially outperform previous biomimetic adhesives obtained by top‐down microfabrication (dry adhesives, friction driven), and represent a unique fluid (aqueous)‐based system with significant anisotropy of adhesion. By using C‐EISA, new emergent properties will be closely tied with the nature of colloids and their hierarchical assemblies.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 it