Simultaneous High Current Density and Selective Electrocatalytic CO<sub>2</sub>‐to‐CH<sub>4</sub> through Intermediate Balancing
Bibliographic record
Abstract
Abstract The electrochemical reduction of CO 2 to CH 4 is promising for carbon neutrality and renewable energy storage but confronts low CH 4 selectivity, especially at high current densities. The key challenge lies in promoting *CO intermediate and *H coupling while minimizing side reactions including C−C coupling and H−H coupling, which is particularly difficult at high current density due to abundant intermediates. Here we report a cooperative strategy to address this challenge using Cu‐based catalysts comprising Cu−N coordination polymer and CuO component that can simultaneously manage the key intermediates *CO and *H. A fast CO 2 ‐to‐CH 4 conversion rate of 3.14 mmol cm −2 h −1 is achieved at 1,300 mA cm −2 with a Faradaic efficiency of 51.7 %. In situ spectroscopy and theoretical calculations show that the increased Cu−Cu distance in the Cu−N coordination polymer component favors multistep *CO hydrogenation over the dimerization, and the CuO component ensures an adequate supply of *H, together contributing to the selective CO 2 ‐to‐CH 4 conversion at high current densities. This work develops a cooperative strategy for the electrosynthesis of CH 4 with simultaneous high current density and high selectivity by rational catalyst design, paving the way for its applications.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".