The effects of physiologically relevant environmental conditions on the mechanical properties of 3D-printed biopolymer nanocomposites
Bibliographic record
Abstract
The demand for synthetic bone graft biomaterials has grown in recent years to alleviate the dependence on natural bone grafts and metal prostheses which are associated with significant practical and clinical issues. Biopolymer nanocomposites are a class of materials that display strong potential for these synthetic materials, especially when processed using additive manufacturing technologies. Novel nanocomposite biomaterials capable of masked stereolithography printing have been developed from functionalized plant-based monomers and hydroxyapatite (HA) with mechanical properties exceeding those of commercial bone cements. However, these biomaterials have not been evaluated under relevant physiological conditions. The effects of temperature (room temperature vs. 37 °C) and water absorption on the physical, surface, and mechanical properties of HA-containing biopolymer nanocomposites were investigated. Exposure to relevant conditions led to substantial impacts on material performance, such as significantly reduced mechanical strength and stiffness. For instance, a composite containing 10 vol% HA and functionalized monomers had 26 and 21% reductions in compressive yield strength and elastic modulus, respectively. After 14 days incubation in phosphate buffered saline, the same composition displayed a 62% decrease in compressive yield strength to 28 MPa. This manuscript demonstrates the relevance and importance of evaluating biomaterials under appropriate physiological conditions throughout their development and provides direction for future material development of HA-containing biopolymer nanocomposites.
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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".