Spectroscopic Determination of Ion-Binding Environment in Alkali-Driven Ionically Cross-Linked Hyaluronic Acid Hydrogels
Bibliographic record
Abstract
Hyaluronic acid’s (HA) interaction with metal cations impacts its phase behavior in an ion-specific, and pH and ion concentration-dependent manner. Under alkaline conditions, bivalent metal cations can cross-link HA into hydrogels. However, the underlying mechanism remains unclear. Herein, we used CMP-NMR and FTIR-ATR to identify interactions that contribute to alkali-driven ionic cross-linking by Mg(II) and Cu(II). We show that the interactions and resulting polymer rigidity differed based on the ion used and correlated with each ion’s propensity for cross-link formation. Interaction with Cu(II) limited the segmental mobility across the entire polymer, while Mg(II) decreased the segmental mobility specifically around the carboxylate group. Further, we found that ionic cross-linking is driven by unidentate coordination with the carboxylate as well as interaction with the deprotonated acetamide nitrogen present only at high pH. Our findings offer a molecular perspective into ionic cross-linking of HA and its dependence on ion identity.
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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".