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 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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".