Cooperative Copper Single‐Atom Catalyst in 2D Carbon Nitride for Enhanced CO<sub>2</sub> Electrolysis to Methane
Bibliographic record
Abstract
Abstract Renewable‐electricity‐powered carbon dioxide (CO 2 ) reduction (eCO 2 R) to high‐value fuels like methane (CH 4 ) holds the potential to close the carbon cycle at meaningful scales. However, this kinetically staggered 8‐electron multistep reduction suffers from inadequate catalytic efficiency and current density. Atomic Cu‐structures can boost eCO 2 R‐to‐CH 4 selectivity due to enhanced intermediate binding energies (BEs) resulting from favorably shifted d‐band centers. In this work, 2D carbon nitride (CN) matrices, viz. Na‐polyheptazine (PHI) and Li‐polytriazine imides (PTI), are exploited to host Cu–N 2 type single‐atom sites with high density (≈1.5 at%), via a facile metal‐ion exchange process. Optimized Cu loading in nanocrystalline Cu‐PTI maximizes eCO 2 R‐to‐CH 4 performance with Faradaic efficiency (FE CH4 ) of ≈68% and a high partial current density of 348 mA cm −2 at −0.84 V vs reversible hydrogen electrode (RHE), surpassing the state‐of‐the‐art catalysts. Multi‐Cu substituted N‐appended nanopores in the CN frameworks yield thermodynamically stable quasi‐dual/triple sites with large interatomic distances dictated by the pore dimensions. First‐principles calculations elucidate the relative Cu–CN cooperative effects between the matrices and how the Cu local environment dictates the adsorbate BEs, density of states, and CO 2 ‐to‐CH 4 energy profile landscape. The 9N pores in Cu‐PTI yield cooperative Cu–Cu sites that synergistically enhance the kinetics of the rate‐limiting steps in the eCO 2 R‐to‐CH 4 pathway.
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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".