Functionally graded titanium implants: Characteristic enhancement induced by combined severe plastic deformation
Bibliographic record
Abstract
Commercially pure titanium was processed by equal channel angular pressing (ECAP) and surface mechanical attrition treatment (SMAT) for the purpose of developing functionally graded titanium used for implants and a gradient structure including nanostructured, deformed and undeformed zones were produced on the samples. In particular, it was aimed to design the gradient-structure in the titanium with enhanced properties by applying 4 ECAP passes to form bulk structure of ultrafine-grains and subsequently subjecting SMAT to the surface of ECAPed samples to produce nanostructured surface region. Microstructural examination was made by electron back scatter diffraction (EBSD). Also, microhardness, nanoindentation, topography, roughness and wettability were evaluated. To examine the biological response, human osteosarcoma cells were cultured in contact with the samples in various time periods and morphology change, cell viability and alkaline phosphate activity were conducted also cell morphology was monitored. EBSD showed development of ultrafine-grained structure after 4 passes of ECAP with an average grain size of 500 nm. Applying SMAT resulted in additional refinement in the ECAP samples, particularly in the subsurface regions to a depth of 112 μm. Furthermore, the SMATed samples showed an enhancement in roughness, wettability and hardness magnitudes. Viability enhanced up to 7% in SMATed + ECAPed sample, although the acceptable cell adhesion, improved cell differentiation and mineralization were seen. The combined use of ECAP and SMAT has shown a good potential for optimizing the design of modern functionally graded medical devices and 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) | 0.001 | 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".