Copper nanoclusters: Selective CO2 to methane conversion beyond 1 A/cm²
Bibliographic record
Abstract
Carbon dioxide offers a unique opportunity as a feedstock for energy production through electrocatalysis. Methane production holds promise for its widespread applications and market demand. However, commercial viability faces challenges of low selectivity, current density, and high applied potential. Efforts to improve methane selectivity while suppressing multi-carbon products, e.g., ethylene, often involve lower alkalinity electrolytes. However, it reduces current density due to increased ohmic resistance without significant gains in the reaction yield. This study utilizes quantum mechanics computations to design a nano-cluster copper catalyst that redirects the reaction pathway from ethylene towards methane, even under alkaline conditions. We achieved a Faradaic efficiency (FE) of 85 %, a current density of 1.5 A/cm 2 , and stability of over 10 hours solely by controlling particle size in copper catalysts. This work paves the way to overcoming current limitations in electrocatalytic methane production and holds broader implications for advancing sustainable CO 2 utilization in energy systems. • DFT computations demonstrate dominant Cu (111) facet at extremely small sizes • Strong chemisorption of ethylene and ethane observed on Cu nanoclusters (Cu NC) • Selectivity shifts from ethylene to methane as particle size decreases • Cu NC with 0.5 nm achieves methane FE of 85 % and 1.2 A/cm 2 partial current density
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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 | 0.000 | 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) | 0.002 | 0.004 |
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; both teacher heads agree on what is shown here.
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".