Effect of Dynamic Chemical Etching on the Pore Structure, Permeability, and Mechanical Properties of Ti-Nb-Zr Scaffolds for Medical Applications
Bibliographic record
Abstract
Abstract Improving the post-processing of metallic porous tissue scaffolds is an essential step to create a new generation of superelastic implants for the replacement of damaged bone tissue. In this study, the dynamic chemical etching technique is applied to improve the permeability and to optimize the porous structure of Ti-Nb-Zr scaffolds fabricated by the powder metallurgy-based space holder technique. The etched scaffolds are characterized in terms of their porous structure geometry, permeability, and mechanical properties. It is shown that an increase in porosity from 49% to 54% during the etching is mainly due to an increase in the number of 100 to 800 μm-diameter pores, from 30% to 50% of them measuring from 100 to 300 μm in size. These changes in the porous structure lead to a significant increase of its permeability, i.e., from (0.1–15) × 10−11 m2 before etching to (44–91) × 10−11 m2, after etching; these permeability ranges corresponding to those of bone tissues. Furthermore, the etched scaffolds show systematically higher yield compressive stresses as compared to the as-sintered scaffolds of equivalent porosities. Finally, the highly permeable etched Ti-Nb-Zr scaffolds with a porosity varying from 40% to 60% exhibit an apparent Young’s modulus ranging from 8.6 to 1.9 GPa and an ultimate compressive strength from 650 to 190 MPa, which can be considered as a promising balance of properties for the potential use of these scaffolds as bone implants.
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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".