Tuning Microscopic Water Orientation in Nickel Single‐Atom Catalyst for Commercial‐Scale CO <sub>2</sub> Electrolysis to CO
Bibliographic record
Abstract
Abstract Electrochemical CO 2 reduction (ECR) to carbon monoxide (CO) offers a sustainable route for fuel and chemical production. Achieving commercial‐scale performance remains difficult, largely due to limited proton supply at high current densities. While single‐atom catalysts exhibit excellent CO 2 ‐to‐CO selectivity, their isolated active sites limit simultaneous optimization of CO 2 activation and water dissociation. Recent studies have highlighted the impact of interfacial water orientation on water dissociation kinetics, but this factor remains underexplored in ECR systems. Here, we demonstrate that modifying Ni–N 4 catalysts with CeO 2 clusters alters the microscopic orientation of interfacial water, thereby enabling industrial‐scale CO production. The CeO 2 ‐modified Ni–N 4 achieves nearly 100% CO Faradaic efficiency at current densities ranging from 50 to 600 mA cm ‒2 in flow cell and maintains 96% at 800 mA cm ‒2 . In a membrane electrode assembly, it sustains over 96% Faradaic efficiency across 50–400 mA cm ‒2 and maintains >95% for 118 h at 150 mA cm ‒2 . Experimental and computational analyses reveal that CeO 2 shifts the water orientation from oxygen‐down to hydrogen‐down configurations, thereby lowering the energy barriers for water dissociation and accelerating protonation. This work demonstrates that interfacial water orientation manipulation is a powerful strategy to enhance the performance of single‐atom catalysts in CO 2 electrolysis.
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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".