Effects of biopolymer functionalization and nanohydroxyapatite heat treatment on the tensile and thermomechanical properties of Bone-Inspired 3D printable nanocomposite biomaterials
Bibliographic record
Abstract
Bone-inspired biopolymer nanocomposite grafts are an alternative to conventional bone substitutes if designed to offer a structure with adequate mechanical properties and biocompatibility. In this study, a set of novel 3D printable bone-inspired nanocomposite biomaterials was developed to investigate the effects of additional cross-linking of the biopolymer matrix and heat treatment of nHA particles on the tensile and thermo-mechanical properties of these nanocomposites used in extrusion-based 3D printing. We observed that additional functionalization of acrylated epoxidized soybean oil (AESO), as the main component of the biopolymer matrix, with additional methacrylate groups (mAESO), increased the strength and elastic modulus of extruded nanocomposites by more than three times, as well as doubled the glass transition temperature due to an increased degree of crosslinking in the functionalized matrices. The mAESO-based nanocomposite filaments demonstrated a strength safety factor of 1.5 against the minimum required standard mechanical properties for bone cement according to ISO standard5833. While heat treatment of nHA reduced the frequency of larger agglomerations, no significant difference in mechanical properties was observed. These novel 3D printable nanocomposite biomaterials with their improved strength, modulus and thermo-mechanical properties could be suitable candidates for fabricating complex ‘by design’ 3D printed grafts and scaffolds for bone reconstruction.
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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".