Boronic acid and diol-containing polymers: how to choose the correct couple to form “strong” hydrogels at physiological pH
Bibliographic record
Abstract
Dynamic covalent hydrogels crosslinked by boronate ester bonds are promising materials for biomedical applications. However, little is known about the impact of the crosslink structure on the mechanical behaviour of the resulting network. Herein, we provide a mechanistic study on boronate ester crosslinking upon mixing hyaluronic acid (HA) backbones modified, on the one hand, with two different arylboronic acids, and on the other hand, with three different saccharide units. Combining rheology, NMR and computational analysis, we demonstrate that carefully selecting the arylboronic-polyol couple allows for tuning the thermodynamics and molecular exchange kinetics of the boronate ester bond, thereby controlling the rheological properties of the gel. In particular, we report the formation of "strong" gels (i.e. featuring slow relaxation dynamics) through the formation of original complex structures (tridentate or bidentate complexes). These findings offer new prospects for the rational design of hydrogel scaffolds with tailored mechanical response.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".