Nanoporous Copper from Electrochemical Dealloying of Brass
Bibliographic record
Abstract
Copper based catalysts have been shown to reduce CO 2 electrochemically into hydrocarbons and oxygenates with high Faradaic efficiencies [1]. The selectivity and the efficiency of CO 2 electroreduction could benefit from morphological studies on Cu nanocatalysts. Among several methods of fabricating Cu nanocatalysts, dealloying, i.e., the selective electrolytic dissolution of a less-noble element from an alloy [2], is one of the most flexible, controllable, and economical methods available to date. The product of this dissolution is a metallic nanoporous material: an interconnected ligament-pore structure with nearly zero net curvature. Building on the previous investigations on dezincification of brass [3], the evolution of nanoporous Cu from dealloying Muntz metal (Cu 60 Zn 40 ) will be presented. The effect of various dealloying parameters, such as anodic potential, temperature, pH, and electrolyte will be resolved via various advanced characterization techniques. [1] C. T. Dinh et al. , “CO 2 electroreduction to ethylene via hydroxide-mediated copper catalysis at an abrupt interface,” Science , vol. 360, no. 6390, pp. 783–787, 2018. [2] R. C. Newman, S. G. Corcoran, J. Erlebacher, M. J. Aziz, and K. Sieradzki, “Alloy corrosion,” MRS Bull. , vol. 24, no. 7, pp. 24–28, 1999. [3] R. C. Newman, T. Shahrabi, and K. Sieradzki, “Direct electrochemical measurement of dezincification including the effect of alloyed arsenic,” Corros. Sci. , vol. 28, no. 9, pp. 873–886, 1988.
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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 | 0.001 |
| 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) | 0.000 | 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".