Enduring CO Electrolysis with Ampere-Level Reaction Rates Using Nickel-Doped Iridium Catalysts
Bibliographic record
Abstract
CO 2 /CO electrolysis offers a scalable pathway for electrosynthesis of multicarbon fuels and chemicals. However, current systems face challenges such as low energy and carbon efficiencies when operated at industrially relevant reaction rates. Our preliminary analysis revealed that the combination of high reaction rates and product crossover-induced pH reduction accelerates anode dissolution, leading to cathode poisoning and, ultimately, performance degradation. Here, we report a strategy to mitigate these challenges by dispersing a low concentration of nickel in an iridium oxide host to promote the stability of iridium species while inhibiting the oxidation of nickel sites. We synthesize a low-valence-nickel in iridium oxide anode material that exhibits high activity and stability for oxygen evolution, while remaining inactive for the oxidation of liquid products migrating from the cathode. In situ soft X-ray photoemission spectroscopy reveals the presence of active sites comprising Ni 2+ and Ir 4+ species. By incorporating this catalyst into an membrane electrode assembly setup, we achieve CO electroreduction on copper with a full-cell energy efficiency of 32% and a carbon efficiency of 73% at 1000 mA per square centimeter, alongside sustained stability over 1000 h of continuous operation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".