Underwater Contact Behavior of Alginate and Catechol-Conjugated Alginate Hydrogel Beads
Bibliographic record
Abstract
Modifying hydrogels with catechol functionality is a promising approach for improving their mechanical and interfacial properties in water, particularly in biological environments. However, the effects of this modification on hydrogels' contact behavior with soft tissues are not well-studied due to the complexity of hydrogels and lack of suitable techniques to probe this behavior. In addition, modification can alter the mechanical properties of hydrogels, resulting in consequences for adhesive strength as well. In this work, we report an investigation of the contact behavior of alginate hydrogels with and without conjugation of catechol functionality, aiming to elucidate the role of catechol modification on wet adhesion of alginates to a model tissue-like material, gelatin. To directly characterize this soft-on-soft contact, which has commonly been a challenge, we developed an indentation-based contact adhesion measurement using alginate hydrogel beads as the testing probe. We found that <3% conjugation of catechol can significantly improve the adhesion of alginate to gelatin by half an order of magnitude, with this adhesion depending heavily on contact time and pH. In contrast, the reduced elastic modulus from modification resulted in lower adhesive strength on rigid substrates. These findings provide valuable insight into the effects of catechol modification of hydrogels, especially in their interaction with tissue-like soft substrates, as well as a simple method for the direct measurement of time- and pH-dependent hydrogel adhesion behavior underwater.
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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".