Achieving the ideal balance between biological and mechanical requirements in composite bone scaffolds through a voxel-based approach
Bibliographic record
Abstract
Achieving successful bone regeneration necessitates the design of scaffolds that meet diverse biological and mechanical requirements, often leading to conflicts in the design parameters. A key conflict arises between scaffold porosity and stiffness. Increasing porosity facilitates cell infiltration and nutrient exchange, promoting bone regeneration. However, higher porosity compromises scaffold stiffness, which is crucial for providing structural support in the defective region. Furthermore, appropriate scaffold stiffness is crucial for preventing stress shielding. Conventional geometry-based design methods utilizing single-phase materials have limited flexibility in resolving such conflicts. To address this challenge, we propose a voxel-based method for designing composite scaffolds composed of hydroxyapatite (HA) and polylactic acid (PLA). Our strategy involves first satisfying primary biological requirements by selecting appropriate porosity, pore shape, and size. Subsequently, scaffold stiffness requirements are met by selecting suitable phase materials and tuning their contents. The study demonstrates that the voxel-based approach effectively balances both biological and mechanical requirements in scaffold design. This method addresses the limitations of traditional designs by achieving an optimal balance between porosity and stiffness, which is crucial for scaffold performance in biomedical applications. Moreover, the scaffolds designed using this method can be manufactured using voxel-based 3D printing technology, which is emerging in the field. Future advancements in voxel-based 3D printing technology will further enhance the feasibility and practicality of this approach for bone tissue engineering 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".