Ultrahigh Mass Activity for Carbon Dioxide Reduction Enabled by Gold–Iron Core–Shell Nanoparticles
Bibliographic record
Abstract
Abstract Wide application of carbon dioxide (CO2) electrochemical energy storage requires catalysts with high mass activity. Alloy catalysts can achieve superior performance to single metals while reducing the cost by finely tuning the composition and morphology. We used in silico quantum mechanics rapid screening to identify Au–Fe as a candidate improving CO2 reduction and then synthesized and tested it experimentally. The synthesized Au–Fe alloy catalyst evolves quickly into a stable Au–Fe core–shell nanoparticle (AuFe-CSNP) after leaching out surface Fe. This AuFe-CSNP exhibits exclusive CO selectivity, long-term stability, nearly a 100-fold increase in mass activity toward CO2 reduction compared with Au NP, and 0.2 V lower in overpotential. Calculations show that surface defects due to Fe leaching contribute significantly to decrease the overpotential.
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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) | 0.002 | 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".