(111) Facet‐oriented Cu<sub>2</sub>Mg Intermetallic Compound with Cu<sub>3</sub>‐Mg Sites for CO<sub>2</sub> Electroreduction to Ethanol with Industrial Current Density
Bibliographic record
Abstract
Abstract The efficient ethanol electrosynthesis from CO2 is challenging with low selectivity at high CO2 electrolysis rates, due to the competition with H2 and other reduction products. Copper‐based bimetallic electrocatalysts are potential candidates for the CO2‐to‐ethanol conversion, but the secondary metal has mainly been focused on active components (such as Ag, Sn) for CO2 electroreduction, which also promote selectivity of ethylene or other reduction products rather than ethanol. Limited attention has been given to alkali‐earth metals due to their inherently active chemical property. Herein, we rationally synthesized a (111) facet‐oriented nano Cu2Mg (designated as Cu2Mg(111)) intermetallic compound with high‐density ordered Cu3‐Mg sites. The in situ Raman spectroscopy and density function theory calculations revealed that the Cu3− −‐Mg− + active sites allowed to increase *CO surface coverage, decrease reaction energy for *CO−CO coupling, and stabilize *CHCHOH intermediates, thus promoting the ethanol formation pathway. The Cu2Mg(111) catalyst exhibited a high FEC2H5OH of 76.2±4.8 % at 600 mA⋅cm−2, and a peak value of |jC2H5OH| of 720±34 mA⋅cm−2, almost 4 times of that using conventional Cu2Mg with (311) facets, comparable to the best reported values for the CO2‐to‐ethanol electroreduction.
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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".