mSLA-based 3D printing of acrylated epoxidized soybean oil - nano-hydroxyapatite composites for bone repair
Why this work is in the frame
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Bibliographic record
Abstract
Structural bone allografts are used to treat critically sized segmental bone defects (CSBDs) as such defects are too large to heal naturally. Development of biomaterials with competent mechanical properties that can also facilitate new bone formation is a major challenge for CSBD repair. 3D printed synthetic bone grafts are a possible alternative to structural allografts if engineered to provide appropriate structure with sufficient mechanical properties. In this work, we fabricated a set of novel nanocomposite biomaterials consisting of acrylated epoxidized soybean oil (AESO), polyethylene glycol diacrylate (PEGDA) and nanohydroxyapatite (nHA) by using masked stereolithography (mSLA)-based 3D printing. The nanocomposite inks possess suitable rheological properties and good printability to print complex, anatomically-precise, ‘by design’ grafts. The addition of nHA to the AESO/PEGDA resin improved the tensile strength and fracture toughness of the mSLA printed nanocomposites, presumably due to small-scale reinforcement. By adding 10 vol% nHA, tensile strength, modulus and fracture toughness (KIc) were increased to 30.8 ± 1.2 MPa (58% increase), 1984.4 ± 126.7 MPa (144% increase) and 0.6 ± 0.1 MPa·m1/2 (42% increase), respectively (relative to the pure resin). The nanocomposites did not demonstrate significant hydrolytic, enzymatic or oxidative degradation when incubated for 28 days, assuring chemical and mechanical stability at early stages of implantation. Apatite nucleated and covered the nanocomposite surfaces within 7 days of incubation in simulated body fluid. Good viability and proliferation of differentiated MC3T3-E1 osteoblasts were also observed on the nanocomposites. Taken all together, our nanocomposites demonstrate excellent bone-bioactivity and potential for bone defect repair.
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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) | 0.001 | 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 it