Ion Valency as a Molecular Switch for Salt‐Resistant Underwater Adhesion
Bibliographic record
Abstract
Abstract Achieving underwater adhesion remains challenging due to the disruption of interfacial interactions by hydration layers and the ionic environment. This study shows how high adhesion in a saline environment can be achieved in adhesive peptide systems relying on π–π and cation‐π interactions using multivalent ions. Monovalent ions (K + ) disrupt native peptide‐peptide interactions, drastically reducing adhesion strength. Conversely, multivalent ions (Mg 2+ and Y 3+ ) enable robust interfacial adhesion by forming stable π‐cation‐π networks, effectively compensating for disrupted native pairings. The adhesion enhancement by Y 3+ is particularly pronounced, highlighting its unique capability for multidentate bridging. Molecular dynamics simulations and quantum mechanical analyses confirm that Y 3+ ions stabilize extended interfacial interactions, enabling stronger stress dissipation during tensile deformation. Additionally, NMR spectroscopy supports these observations by demonstrating significant cation‐dependent perturbations of aromatic (Phe) and cationic (Lys) peptide residues. A thermodynamic model further elucidates the competitive binding dynamics underpinning adhesion modulation and capturing all experimental trends. This work provides detailed molecular insights into ion valency effects on cation‐π mediated underwater adhesion, guiding the development of bio‐inspired materials with tailored ionic responsiveness suitable for biomedical and technological applications in saline environments.
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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".