Covalently cross‐linked hydrogels: Mechanisms of nonlinear viscoelasticity
Bibliographic record
Abstract
Abstract Gelatin‐based hydrogels have been widely used in tissue engineering, three‐dimensional cell culture, drug delivery, and cell therapy. The mechanical behaviour of hydrogels combined with their chemical properties determines their functionality and efficacy. With respect to the mechanical behaviour of hydrogels, the vast majority of publications have reported their linear viscoelastic response. However, for practical conditions in the body, these materials experience large deformations beyond the linear viscoelastic limit. Herein, to mimic practical conditions and to evaluate the mechanical response of the hydrogels subjected to large deformations, we report inter‐ and intra‐cycle nonlinear viscoelastic behaviour of a gelatin methacryloyl (GelMA) hydrogel with different concentrations of the hydrogel precursor (10%–20% [w/v]) under large amplitude oscillatory shear deformation. To achieve this, we used a novel technique by chemically bonding the hydrogels to treated glass slides, which were attached to the oscillating metal plates using a double‐sided tape to alleviate any error arising from wall slip during rheological measurements. The results show that the elasticity of the covalently cross‐linked hydrogels at large deformations obeys a nonlinear force‐extension law and that the viscous intra‐cycle nonlinearity at moderate deformations stems from the dual cross‐linked (DC; i.e., physical and chemical) nature of the GelMA hydrogel. It was also shown that viscoelastic parameters can be tuned by the concentration of the hydrogel precursor, that is, yield stress increased from 2.6–7.1 kPa, critical strain amplitude decreased from γ 0 = 100%–70%, and the onset of inter‐cycle nonlinearity shifted from γ 0 = 50%–20% upon increasing the concentration of the hydrogel precursor. These insights have important implications for the rational development of hydrogel‐based biomaterials to design biocompatible scaffolds in tissue engineering applications.
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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".