Steering the Selectivity of Electrochemical CO<sub>2</sub> Reduction on the Cu Catalyst via the Interplay between the Electrode Morphology and Electrolyte Anion Identity
Bibliographic record
Abstract
Electrochemical carbon dioxide (CO 2 ) reduction (ECR) holds promise as a viable pathway for the generation of fuels and chemicals. Several strategies have been explored to enhance the product selectivity of ECR on copper (Cu) catalysts. A systematic approach to optimize the local reaction microenvironment, however, remains elusive. Engineering the electrode structure and reaction microenvironment is a facile but effective strategy for steering the product selectivity of ECR reactions and can enable the rational design of highly selective Cu electrodes. Herein, we demonstrate that the synergy between an optimized Cu gas diffusion electrode (GDE) morphology and electrolyte anion identity can steer ECR product selectivity toward ethylene (C 2+ ) or methane via the local CO 2 availability, pH, and electrode morphology regulation. We show that using a relatively thin 100 nm Cu catalyst layer (CL) sputtered on an optimized macropore-sized hydrophobic poly(tetrafluoroethylene) substrate promotes methane selectivity at high reaction rates. We achieved a methane partial current density of 126 mA cm –2 and a Faradaic efficiency (FE) of 42%. In contrast, a relatively thick 500 nm Cu CL favors ethylene production, reaching a high FE of 52% at 250 mA cm –2 (with a total C 2+ value of 77%) in a near-neutral KHCO 3 electrolyte. Utilizing KI electrolyte significantly enhances methane selectivity, achieving ca. 56% at a partial current density of 168 mA cm –2 while effectively suppressing the hydrogen evolution reaction (HER) on the thin CL. Furthermore, on the relatively thick CL, a higher C 2+ FE of 84% was achieved at 250 mA cm –2, demonstrating the impact of electrolyte anion identity and CL thickness on product selectivity in ECR. In addition, we find that a further increase in the Cu CL thickness does not result in a superior C 2+ performance in KI compared to the KHCO 3 electrolyte. Our result highlights the critical role of the interplay between Cu electrode morphology and the electrolyte anion identity, which can facilitate efficient CO 2 mass transport, enable selective Cu sites, and tune local pH – thereby steering ECR product selectivity.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".