Graphene Oxide/Elastin Nanostructure-Based Membranes for Bone Regeneration
Bibliographic record
Abstract
Nacre has an excellent combination of strength and toughness due to its “brick-and-mortar” layered structure. Graphene oxide (GO) is an ideal candidate as a “brick” material due to its two-dimensional structure, outstanding ultimate strength, and Young’s modulus. GO is also able to stimulate osteogenesis, which suggests the potential application of nacre-like GO-based nanocomposites in bone regeneration. Most nacre-like GO-based nanocomposites developed thus far focus on the simultaneous enhancement of strength and toughness, with only three studies published to date investigating the osteogenic potential of the nanocomposites. All three studies used a ternary system with GO as the brick, chitosan as the mortar, and hydroxyapatite or calcium silicate as the bioactive additive to stimulate apatite formation and bone integration. Herein we introduce a binary nacre-like nanocomposite based on GO and elastin for bone regeneration. Elastin, as an extracellular matrix protein, confers elasticity to tissues. Elastin also initiates mineral deposition in the aorta, suggesting its potential to induce mineral formation during bone regeneration. Hence, elastin acts as the bioactive additive in addition to its function as the mortar phase. The nacre-like GO/elastin nanocomposite membranes can be fabricated with a simple evaporation approach; they have a compact “brick-and-mortar” multilayered structure, a tensile strength of 93 ± 10 MPa, and Young’s modulus of 13.4 ± 0.4 GPa, comparable with cortical bone. Elastin incorporation promotes mineralization in simulated body fluid and enhances the proliferation as well as osteogenic differentiation of mouse bone marrow mesenchymal stem cells compared to pristine GO membranes. These results suggest that nacre-like GO/elastin membranes are promising materials for biomedical applications in bone regeneration, including bone implants, barrier membranes for guided bone regeneration, and coatings for implant materials.
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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 | 0.000 | 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) | 0.000 | 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".