A Gold–Palladium Nanoparticle Alloy Catalyst for CO Production from CO<sub>2</sub> Electroreduction
Bibliographic record
Abstract
Electrochemical CO2 reduction reaction (CO2RR) is a promising technology for combining CO2 reutilization and renewable electricity storage. For economic feasibility, better catalysts are required to overcome current limitations such as high overpotentials, poor faradaic efficiencies (FEs), and low current densities. Herein, size‐ and composition‐controlled gold (Au)–palladium (Pd) bimetallic alloy nanoparticles prepared by a metal vapor synthesis technique together with the monometallic Au and Pd equivalent materials are investigated. X‐ray diffraction and high‐angle annular dark‐field scanning transmission electron microscopy energy‐dispersive X‐ray spectroscopy analyses confirm the Au–Pd alloy formation with an average atomic ratio of 76 Pd wt% and 24 Au wt%. These bimetallic and the monometallic catalysts are characterized and tested for the electroreduction of CO2 in electrochemical cells and also in a complete CO2 electrolysis cell. Analysis of the reduction products shows a 100% CO2RR selectivity for CO. The FE for CO with respect to H2 as high as 90% is obtained with Au–Pd/C by tuning the electrode structure. The Au/C and Pd/C catalysts also show a better selectivity for CO with no evidence of other CO2RR products. The Au–Pd alloy formation improves the FE of Pd for CO by suppressing the parasitic H2 evolution reaction.
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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".