Bone-inspired lattice structures for biomedical applications: Design, pore network analysis, and mechanical performance
Bibliographic record
Abstract
This study aims to design metamaterials that replicate trabecular bone anisotropic structure and mechanical performance for bone replacement applications. While triply periodic minimal surface (TPMS) gyroid structures like sheet (SHN) and solid (SON) networks have been explored for bone replacement, their uniform designs fail to fully capture bone’s anisotropy. To address this, we propose a hybrid (HYB) strategy as a more effective solution. We investigate bone-inspired structures by comparing human proximal femur models from micro-computed tomography (CT) images. A 3D lattice was extracted from the trabecular bone region, and compared with TPMS gyroid structures: SHN, SON, and HYB. All structures were standardized to a ∼33.75% relative density, ensuring a fair comparison, and their anisotropic behavior was analyzed using micromechanical homogenization. Finite element (FE) simulations under various loading conditions confirmed their elastic response and anisotropy. Additionally, the anisotropic behavior of bone material was also analyzed using bone analysis, while pore network modeling (PNM) characterized key pore parameters. Experimental validation included 3D printing via fused deposition modeling with carbon fiber-reinforced polylactic acid, followed by compression testing and elastoplastic FE simulations. Our results show that, compared with uniform TPMS structures, the HYB design closely replicates bone, making it a strong candidate for bone replacement 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".