Tuning C–C Coupling and Selectivity in CO <sub>2</sub> Electrochemical Reduction Reaction via Pyramidal Dilute Sn–Cu Alloy
Bibliographic record
Abstract
The electrochemical conversion of carbon dioxide (CO 2 ) into value-added fuels is emerging as a promising strategy to combat climate change and support carbon neutrality. Despite recent advances, the selective generation of higher-order hydrocarbons (C 2 + products) remains a significant challenge due to kinetic and thermodynamic limitations. In this study, we report the synthesis of electrocatalysts comprised of a pyramidal dilute Sn–Cu alloy, fabricated via electrodeposition onto titanium substrates. The pure Cu sample showed the lowest surface roughness with smooth, spherical particles, while the addition of trace Sn was crucial in transforming the morphology to faceted pyramidal structures. Incorporating 1 at % Sn into Cu nanopyramids significantly enhances catalytic activity and selectivity toward ethylene (C 2 H 4 ) production. Electrochemical tests reveal that the Cu 99 Sn 1 catalyst achieves a Faradaic efficiency of 37% for ethylene at −0.8 V versus RHE, alongside operational stability over 12 h of continuous electrolysis. The improved performance of the Cu 99 Sn 1 nanostructures is attributed to multiple synergistic effects. First, alloying with Sn modulates the electronic structure of Cu, stabilizing key *CO intermediates that are critical for C–C coupling while concurrently suppressing the hydrogen evolution reaction (HER) by limiting H + adsorption. Second, the unique pyramid-shaped morphology introduces high-index facets, abundant edge sites, and a high density of surface defects. These characteristics contribute to an enhanced active surface area, which is known to promote favorable adsorption configurations and accelerate reaction kinetics. Complementary density functional theory (DFT) calculations further support the experimental findings, showing that the pyramidal geometry modulates the local electronic environment and optimizes adsorption energies to facilitate C–C bond formation while inhibiting HER. This work highlights the powerful interplay between atomic-level alloying and nanostructural engineering in tailoring catalyst functionality for CO 2 electroreduction. The findings offer a promising route toward efficient, selective, and sustainable carbon utilization technologies.
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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) | 0.000 | 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 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".