Nanopatterning of Transition Metal Surfaces <i>via</i> Electrochemical Dimple Array Formation
Bibliographic record
Abstract
Nanoscale surface patterning is of great importance for applications ranging from catalysts to biomaterials. We show the formation of ordered nanoscale dimple arrays on titanium, tungsten, and zirconium during electropolishing, demonstrating versatility of a process previously only reported for tantalum. This is a rare example of an electrochemical pattern formation process that can be translated to other materials. The dimpled surfaces have been characterized with scanning electron microscopy, transmission electron microscopy, atomic force microscopy, and X-ray photoelectron spectroscopy, and electrochemical conditions were optimized for each material. While conditions for titanium and tungsten resemble those for tantalum, zirconium requires a different type of electrolyte. Given the appropriate electropolishing chemistry, formation of these patterns should be possible on any metal surface. The process is very robust on homogeneous surfaces, but sensitive to inhomogeneities in chemical composition, such as in the case of differentially etched alloys. An alternative process for some materials such as platinum is the coating of a dimpled substrate with a thin film of the required material.
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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".