Optimal Icosahedral Copper-Based Bimetallic Clusters for the Selective Electrocatalytic CO2 Conversion to One Carbon Products
Bibliographic record
Abstract
Electrochemical CO2 reduction reactions can lead to high value-added chemical and materials production while helping decrease anthropogenic CO2 emissions. Copper metal clusters can reduce CO2 to more than thirty different hydrocarbons and oxygenates yet they lack the required selectivity. We present a computational characterization of the role of nano-structuring and alloying in Cu-based catalysts on the activity and selectivity of CO2 reduction to generate the following one-carbon products: carbon monoxide (CO), formic acid (HCOOH), formaldehyde (H2C=O), methanol (CH3OH) and methane (CH4). The structures and energetics were determined for the adsorption, activation, and conversion of CO2 on monometallic and bimetallic (decorated and core@shell) 55-atom Cu-based clusters. The dopant metals considered were Ag, Cd, Pd, Pt, and Zn, located at different coordination sites. The relative binding strength of the intermediates were used to identify the optimal catalyst for the selective CO2 conversion to one-carbon products. It was discovered that single atom Cd or Zn doping is optimal for the conversion of CO2 to CO. The core@shell models with Ag, Pd and Pt provided higher selectivity for formic acid and formaldehyde. The Cu-Pt and Cu-Pd showed lowest overpotential for methane formation.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".