Enhanced Mechanical Properties of 3D Printed Nanocomposites Composed of Functionalized Plant-Derived Biopolymers and Calcium-Deficient Hydroxyapatite Nanoparticles
Bibliographic record
Abstract
The combination of plant-derived polymer resins and mineral-based nanoparticles into three dimensional (3D) printable, high-performance nanocomposites suggests a means to improve the sustainability profile of rapid prototyping and additive manufacturing. In this work, our previously published nanocomposite biomaterial system of acrylated epoxidized soybean oil (AESO) and polyethylene glycol (PEGDA) diluent composited with calcium-deficient hydroxyapatite (nHA) nanorods was improved by the substitution of AESO with methacrylated AESO (mAESO) and the partial substitution of PEGDA with isosorbide methacrylate (IM). mAESO was used to increase the degree of crosslinking and reduce the ink viscosity. IM was synthesized by reacting the hydroxyl groups on isosorbide with methacrylic anhydride. The effects of partially replacing PEGDA with IM on the rheology and printability of the nanocomposite inks and the mechanical properties of the resulting nanocomposite materials were quantified. These masked stereolithography (mSLA) printed nanocomposites have greatly improved mechanical properties (tensile strength, Young’s modulus, and Mode-I fracture toughness) due to the shift to mAESO and IM. IM greatly improved the tensile fracture strength and Young’s modulus of the nanocomposites by acting as a reactive diluent and as a stiff segment in the polymer system. Dynamic mechanical analysis revealed that the glass transition temperature of the nanocomposite increased due to the addition of IM. However, IM decreased the strain-at–fracture, making the nanocomposites more brittle. This study demonstrates the development of high-performance mAESO-IM-nHA-based novel nanocomposites that can be easily 3D printed using desktop mSLA, suggesting a facile path forward to improved sustainability in rapid prototyping and additive manufacturing using nanocomposites for a broad range of 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) | 0.000 | 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".