Biomimetic Non-ergodic Aging by Dynamic-to-covalent Transitions in Physical Hydrogels
Bibliographic record
Abstract
Hydrogels are soft materials engineered to suit a multitude of applications that exploit their tunable mechanochemical properties. Dynamic hydrogels employing noncovalent, physically cross-linked networks dominated by either enthalpic or entropic interactions enable unique rheological and stimuli-responsive characteristics. In contrast to enthalpy-driven interactions that soften with increasing temperature, entropic interactions result in largely temperature-independent mechanical properties. By engineering interfacial polymer-particle interactions, we can induce a dynamic-to-covalent transition in entropic hydrogels that leads to biomimetic non-ergodic aging in the microstructure without altering the network mesh size. This transition is tuned by varying temperature and formulation conditions such as pH, which allows for multivalent tunability in properties. These hydrogels can thus be designed to exhibit either temperature-independent metastable dynamic cross-linking or time-dependent stiffening based on formulation and storage conditions, all while maintaining structural features critical for controlling mass transport, akin to many biological tissues. Such robust materials with versatile and adaptable properties can be utilized in applications such as wildfire suppression, surgical adhesives, and depot-forming injectable drug delivery systems.
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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".