Zero-Gap Electrolyzers Accelerate Reconstruction of Cu<sub>2</sub>O-Derived Catalysts under CO<sub>2</sub> Reduction
Bibliographic record
Abstract
To scale carbon dioxide reduction (CO 2 R), establishing a structure–property–performance relationship of the catalyst under the reaction conditions is a priority. Particularly in membrane electrode assembly (MEA) electrolyzers, knowledge about the valence state and coordination environment of the catalyst is of value yet limited. We developed an MEA electrolyzer that utilizes X-ray absorption spectroscopy to investigate the structural evolution of Cu 2 O-derived catalysts under CO 2 R and compare the same catalysts in a flow cell. Additionally, we study the influence of CO reduction and incorporating Ag on the reconstruction of the catalyst. We find that the strong reduction environment in the MEA and feeding CO leads to reconstruction of Cu 2 O particles, favoring higher coordination and lower oxidation states, which coincides with a shift in the reaction selectivity from C 2+ to hydrogen. Conversely, incorporating small amounts of Ag in the catalyst restricts the reconstruction. These findings advocate for in situ studies in zero-gap electrolyzers.
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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".