Mechanical properties of nanocomposite biomaterials improved by extrusion during direct ink writing
Bibliographic record
Abstract
In this study, single filaments of acrylated epoxidized soybean oil (AESO)/polyethylene glycol diacrylate (PEGDA)/nanohydroxyapatite (nHA)-based nanocomposites intended for bone defect repair have displayed significant improvement of their mechanical properties when extruded through smaller needle gauges before UV curing. These nanocomposite inks can be deposited layer-by-layer during direct ink writing (DIW) - a form of additive manufacturing. Single filaments were prepared by extruding the nanocomposite ink through needles with varying diameters from 0.21 mm to 0.84 mm and then UV cured. Filaments and cast specimens were tensile tested to determine elastic modulus, strength and toughness. The cured nanocomposite filaments were further characterized using thermogravimetric analysis (TGA), differential scanning calorimetry (DSC), Fourier-transform infrared (FTIR) spectroscopy, and scanning electron microscopy (SEM). SEM confirmed that the hydroxyapatite nanoparticles were well dispersed in the polymer matrices. The ultimate tensile strength and moduli increased as the diameter of the extrusion needle was decreased. These correlated with increased matrix crystallinity and fewer defects. For instance, filaments extruded through 0.84 mm diameter needles had ultimate tensile stress and modulus of 26.3 ± 2.8 MPa and 885 ± 100 MPa, respectively, whereas, filaments extruded through 0.21 mm needles had ultimate tensile stress and modulus of 48.9 ± 4.0 MPa and 1696 ± 172 MPa, respectively. This study has demonstrated enhanced mechanical properties resulting from extrusion-based direct ink writing of a new AESO-PEGDA-nHA nanocomposite biomaterial intended for biomedical applications. These enhanced properties are the result of fewer defects and increased crystallinity. A means of achieving mechanical properties suitable for repairing bone defects is apparent.
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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) | 0.000 | 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 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".