Effect of Alkali-Acid-Heat Chemical Surface Treatment on Electron Beam Melted Porous Titanium and Its Apatite Forming Ability
Bibliographic record
Abstract
Advanced additive manufacturing techniques such as electron beam melting (EBM), can produce highly porous structures that resemble the mechanical properties and structure of native bone. However, for orthopaedic applications, such as joint prostheses or bone substitution, the surface must also be bio-functionalized to promote bone growth. In the current work, EBM porous Ti6Al4V alloy was exposed to an alkali acid heat (AlAcH) treatment to bio-functionalize the surface of the porous structure. Various molar concentrations (3, 5, 10M) and immersion times (6, 24 h) of the alkali treatment were used to determine optimal parameters. The apatite forming ability of the samples was evaluated using simulated body fluid (SBF) immersion testing. The micro-topography and surface chemistry of AlAcH treated samples were evaluated before and after SBF testing using scanning electron microscopy and energy dispersive X-ray spectroscopy. The AlAcH treatment successfully modified the topographical and chemical characteristics of EBM porous titanium surface creating nano-topographical features ranging from 200-300 nm in size with a titania layer ideal for apatite formation. After 1 and 3 week immersion in SBF, there was no Ca or P present on the surface of as manufactured porous titanium while both elements were present on all AlAcH treated samples except those exposed to 3M, 6 h alkali treatment. An increase in molar concentration and/or immersion time of alkali treatment resulted in an increase in the number of nano-topographical features per unit area as well as the amount of titania on the surface.
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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".