Biomimetic Ion‐Orchestrated Hierarchical Armored Hydrogel Coating for Robust and Multifunctional Surface Protection
Bibliographic record
Abstract
Abstract Engineering hydrogel coating with hierarchical architecture offers a promising pathway to multifunctional surface protection. However, maintaining structural integrity in such a layered structured system remains a critical challenge due to interfacial mechanical mismatches. Such fragile structures will ultimately compromise their surface antifouling and anticorrosion performance. Inspired by the intriguing skin‐toughening behavior of marine sponges, an ion‐orchestrated structural engineering strategy is presented to modulate the surface microstructure of hydrogel coatings, enabling the rapid assembly of a surface‐confined “armor layer” directly from the hydrogel matrix without compromising its overall structural integrity. Molecular force measurements reveal that diffused metal ions coordinate with embedded tannic acid engineered cellulose nanocrystals (TA/CNC), driving their directional migration toward the hydrogel surface and triggering the in situ self‐assembly as a high‐modulus armor layer (over 700 kPa) that locally reinforces the mechanical robustness of the hydrogel coating. Moreover, this surface‐confined hydrophilic armor layer acts as a multifunctional barrier, suppressing over 97% of oil droplets, protein, and biofluid adhesion, and enhancing surface corrosion resistance by reducing corrosion inhibitor leaching by 84.5% through a unique surface pore‐sealing effect. These findings provide a new paradigm for ion‐driven nanoscale reorganization to tailor surface functionality, paving the way for next‐generation, sustainable protective coatings across diverse applications.
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How this classification was reachedexpand
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 | 0.000 |
| 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 teacher head, 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".