Roughening and Long-Range Nanopatterning of Au(111) through Potential Cycling in Aqueous Acidic Media
Bibliographic record
Abstract
Electrochemical treatment of Au(111) in aqueous H2SO4 solution by repetitive application of oxide formation-reduction cycles (OFRC) generates nanopatterned surfaces with long-range order. The pattern development depends on the lower and upper potential limits (EL, EU), the number (n) of OFRCs, and the potential scan rate (s). Surface patterning of Au(111) initially (n = 1-2) generates small islands and holes that are one atomic step in height. As n increases to 5, the number of islands decreases and the holes become larger; after n = 10 OFRCs, the islands become inexistent and large, randomly distributed holes are observed. Increase of OFRCs to n = 20 generates surface structures that reside within three atomic layers and resemble phase separation through a spinodal decomposition mechanism. As the number of OFRCs rises to n = 50, a network of interconnected islands and holes emerges; the islands and holes are two-three atomic steps in height, and are located within topmost five monolayers. Further increase of the number of OFRCs to n = 100 creates a network of interconnected trigonal pyramids that are pointed in the same direction. The size of the pyramids depends on the electrolyte composition and the number of OFRCs. In the case of n = 100, the pyramids are 12-25 nm in base length and 0.4-1.6 nm in height in 0.1 M aqueous H2SO4, and 20-50 nm in base length and 0.8-1.6 nm in height in 0.1 M aqueous HNO3. The number of OFRCs and scan rate play an important role in patterning of Au(111), and complete nanopattern development requires a large number of OFRCs and low scan rates.
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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".