Multifunctional Underwater Adhesive Film Enabled by a Single-Component Poly(ionic liquid)
Bibliographic record
Abstract
Tremendous efforts have been devoted to exploiting synthetic wet adhesives for real-life applications. However, developing low-cost, robust, and multifunctional wet adhesive materials remains a considerable challenge. Herein, a wet adhesive composed of a single-component poly(ionic liquid) (PIL) that enables fast and robust underwater adhesion is reported. The PIL adhesive film possesses excellent stretchability and flexibility, enabling its anchoring on target substrates regardless of deformation and water scouring. Surface force measurements show the PIL can achieve a maximum adhesion of 56.7 mN·m –1 on diverse substrates (both hydrophilic and hydrophobic substrates) in aqueous media, within ∼30 s after being applied. The adhesion mechanisms of the PIL were revealed via the force measurements, and its robust wet adhesive capacity was ascribed to the synergy of different non-covalent interactions, such as of hydrogen bonding, cation−π, electrostatic, and van der Waals interactions. Surprisingly, this PIL adhesive film exhibited impressive underwater sound absorption capacity. The absorption coefficient of a 0.7 mm-thick PIL film to 4–30 kHz sound waves could be as high as 0.80–0.92. This work reports a multifunctional PIL wet adhesive that has promising applications in many areas and provides deep insights into interfacial interaction mechanisms underlying the wet adhesion capability of PILs.
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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) | 0.001 | 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".