Distribution of the Retained Less-Noble Element in Dealloyed Materials
Bibliographic record
Abstract
Electrolytic dealloying of a solid-solution alloy is usually understood to involve a relatively large content of more-noble element in the alloy (e.g. 25%), from which starting point it is natural that dealloying will produce a connected nanoporous structure. But it has been known for a long time, since the work of H.W. Pickering, that starting alloys such as Cu-10 at%Au can be dealloyed to produce fully connected Au-rich dealloyed layers, at least to a certain thickness. Clearly not all the Cu could have been removed during this process, and Pickering demonstrated not only that the dealloyed material retained much of its Cu, but that there was a compositional gradient, or at least a range of dealloyed compositions, within the material. In the present work we have used a 95Ag-5Au (at%) alloy and have attempted to correlate the dealloying behaviour (ligament size in the nanoporous layer; true surface area) with Kinetic Monte Carlo simulations of the dealloying process. The results are analogous, with some areas for further quantitative development. A related behaviour in stainless steels is discussed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".