A multiscale design and fabrication approach to create biomimetic tunable implants using additive manufacturing
Bibliographic record
Abstract
Canadians across the country rely on hard-tissue implants such as hip, knee, and dental implants. Rises in aging populations are further increasing these needs, where diseases prevalent in elderly patients, such as osteoporosis (OP), contribute to these demands and can complicate osseointegration processes. To better serve these populations, current biomaterials used in bone implants must be improved, which can suffer failure from effects such as stress shielding, instability, inflammation, and aseptic loosening. In this work, a multiscale design and manufacturing approach using additive manufacturing (AM) was introduced to design tunable porous scaffolds with biomimetic hierarchical features. For tunable scaffold design, literature-driven design parameters found to be suitable for enhancing osseointegration and appropriate for AM were consulted. A Voronoi tessellation strategy was adopted to create dynamically tunable structures using a parametric modelling approach. In-model topology evaluation metrics (e.g. porosity, strut diameter, node connectivity, and intertrabecular angles) were included to provide designers insight into scaffold mimetics to different bone structures, key for considering site-specific locations and conditions, such as healthy versus OP bone. Current progress related to the materials and mechanical assessment of as-printed scaffold structures is shared. Select scaffold structures were produced using state-of-the-art laser powder bed fusion with Ti-6Al-4V. AM introduced micro-roughness, while chemical etching will induce engineered nanoscale texturing, which is anticipated to improve cellular adhesion and bone growth. Laser profilometry and scanning electron microscopy characterized surface roughness and morphology, while the influence of AM and computer-aided design (CAD) parameters on material properties in future will be assessed in mechanical testing. Overall, this work builds a foundation for the design of innovative biomimetic porous implants that can be tuned to meet patient-specific needs.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".