Mussels Put Their Best “pH”oot Forward: Importance of pH in Formation of Biological and Bio‐Inspired Materials
Bibliographic record
Abstract
Abstract Mussel byssus offers bio‐inspired designs for advanced adhesives, coatings, and supramolecular hydrogels. Solidification of secreted proteins into mechanically robust fibers is triggered by a pH jump from acidic fluid condensates to basic seawater conditions driving protein crosslinking by metal coordination with 3,4‐dihydroxyphenylalanine (DOPA). Knowledge of the dynamic and localized pH changes during secretion is currently lacking. Yet, this information is crucial for controlling the gelation and properties of mussel‐inspired DOPA‐modified materials. Here, an iridium oxide‐based pH microsensor combining high mechanical stability and spatial resolution is used to measure the acidic pH during byssus secretion and to map mussel‐inspired hydrogels revealing compositional heterogeneity resulting in mechanically distinct regions. Within the hydrogel, high pH values >12 are measured which are detrimental to DOPA due to its strong propensity for oxidation, which adversely alters gel properties. This demonstrates the need to improve preparation methods of mussel‐inspired adhesive materials to more closely mimic native processes.
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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".