Controlling Mechanical Properties of Cell-Laden Hydrogels by Covalent Incorporation of Graphene Oxide
Bibliographic record
Abstract
Graphene-based materials are useful reinforcing agents to modify the mechanical properties of hydrogels. Here, an approach is presented to covalently incorporate graphene oxide (GO) into hydrogels via radical copolymerization to enhance the dispersion and conjugation of GO sheets within the hydrogels. GO is chemically modified to present surface-grafted methacrylate groups (MeGO). In comparison to GO, higher concentrations of MeGO can be stably dispersed in a pre-gel solution containing methacrylated gelatin (GelMA) without aggregation or significant increase in viscosity. In addition, the resulting MeGO-GelMA hydrogels demonstrate a significant increase in fracture strength with increasing MeGO concentration. Interestingly, the rigidity of the hydrogels is not significantly affected by the covalently incorporated GO. Therefore, this approach can be used to enhance the structural integrity and resistance to fracture of the hydrogels without inadvertently affecting their rigidity, which is known to affect the behavior of encapsulated cells. The biocompatibility of MeGO-GelMA hydrogels is confirmed by measuring the viability and proliferation of the encapsulated fibroblasts. Overall, this study highlights the advantage of covalently incorporating GO into a hydrogel system, and improves the quality of cell-laden hydrogels.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".