Imparting conformational memory for material adhesion
Bibliographic record
Abstract
Adhesion between similar and dissimilar materials is essential to many biological systems and synthetic materials, devices, and machines. Since the inception of adhesion science more than five decades ago, adhesion to a surface has long been recognized as beyond two-dimensional. Similarly, molecular conformation - the three-dimensional arrangement of atoms in a molecule - is ubiquitous in biology and fundamental to the binding of biomolecules. However, the connection between these concepts, which could link molecular conformation in biology to micro- and macroscopic adhesion in materials science, remains elusive. Herein, we examine this connection by manipulating the molecular conformation of a mussel-inspired universal coating, which imparts a memory for recognizing different hydrogels. This approach leads to significantly (several fold) increased interfacial adhesion between the coating and hydrogels across a broad range of length scales, from molecular to macroscopic. Furthermore, we demonstrate that imparting memory is a general and facile noncovalent approach for enhancing interfacial adhesion that, with suitable energy dissipation, can be used for the bonding of materials.
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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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".