Gold-Copper Metal Alloys: Crystalline Materials for the CO<sub>2</sub> Reduction Reaction
Bibliographic record
Abstract
Chemical fixation of CO2 into fuels has been the focus of intense research interest due to growing energy demands and the cumulative environmental impact associated with fossil fuel consumption. Reduction of CO2 to CO and hydrocarbons therefore represents an opportunity to close the carbon cycle. Despite much progress, improvements in CO2 reduction activity, and catalyst stability are required to transform existing catalyst materials into viable candidates for this purpose. Here, we describe our efforts to develop new gold-copper alloy materials for application in CO2 reduction. We employ recent metal deposition technology developed in our laboratory that enables single-crystal epitaxial noble metal deposition from solutions containing their simple metal salts. The Au-Cu metal pair has been selected due to its chemical stability and catalytic properties. Co-deposition from solutions comprised of Au- and Cu-containing metal salts allows the fabrication of Au-Cu alloys of controlled composition. We describe the fabrication and characterization of Au-Cu alloy materials using a wide range of techniques including X-ray diffraction, X-ray photoelectron spectroscopy, scanning electron microscopy, and high resolution transmission electron microscopy. We have also investigated the alloy elelctrocatalytic activity through linear sweep voltammetry and chronoamperometry to establish catalytic activity and correlate the activity with catalyst structure and composition.
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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 | 0.001 |
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".