Direct Ink Writing of Graphene Oxide Reinforced 13-93B1 Bioactive Glass Scaffolds for Bone Tissue Engineering Applications
Bibliographic record
Abstract
Abstract Graphene-reinforced bioactive glass scaffolds have gained significant attention in the field of bone tissue engineering due to their unique combination of mechanical strength, bioactivity, and electrical conductivity. Additive manufacturing techniques, such as 3D printing, provide a versatile platform for fabricating these scaffolds with precise control over their architecture and composition. Consequently, in this work, we have fabricated graphene oxide (GO)-reinforced 13-93B1 bioactive glass scaffold using the direct ink writing technique. A Pluronic F-127- based ink was prepared for scaffold fabrication, and its rheological properties were assessed for shear thinning behaviour, structural support, and recovery. Further, the fabricated scaffolds were characterized using micro-computed tomography, scanning electron microscopy, and energy dispersive x-ray spectroscopy. Additionally, computational fluid dynamics simulations with Dulbecco’s modified eagle medium and blood were performed to evaluate the perfusion kinetics of the scaffolds. The inclusion of GO enhanced the compressive strength of the fabricated scaffolds by ∼225%. The morphological characterization based on micro-computed tomography showed that additively manufactured scaffolds have appropriate porosity, pore size, pore throat size, and interconnectivity. The live-dead assay results showed no cytotoxicity towards C2C12 mouse myoblast cells. Also, cell adhesion and cell viability results show better cell growth on the nanocomposite scaffolds. Overall, the fabricated scaffold is found suitable for bone 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".