Ordered nano-scale dimple pattern formation on a titanium alloy (Ti-6Al-4V)
Bibliographic record
Abstract
Due to the many applications of nanostructured surfaces – including in biomaterials – there is a strong interest in cost- and time-efficient methods for their fabrication. Previously, our group established a simple electrochemical method generating nanoscale patterns on large areas of a number of different metal surfaces. They consist of dimples that are around 6-10 nm deep and hexagonally closed packed with a tunable periodicity of around 50 nm. Ordering requires careful tuning of the surface chemistry, which makes the translation of these findings to multi-component alloys non-obvious. Here, we demonstrate for the first time that such a pattern can also be achieved on the surface of an alloy, namely Ti-6Al-4V. This alloy is of particular interest for biomedical implants. While dimple formation on the main component metals titanium and aluminum has previously been reported (albeit under conditions that differ from each other), we now also report dimple formation on pure vanadium surfaces to occur under very different conditions. Dimple formation occurs preferentially on the (dominant) α-phase grains of the alloy. The size of dimples of the alloy material is subject to the electropolishing potential, electrolyte concentration and surface chemical composition, which gives us the opportunity to control the surface features. Since a main application of this alloy are biomedical implants, this level of control will be an important tool for accommodating cell growth.
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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".