Catalyst Regeneration via Chemical Oxidation Enables Long-Term Electrochemical Carbon Dioxide Reduction
Bibliographic record
Abstract
Electrochemical CO 2 reduction (ECR) with industrially relevant current densities, high product selectivity, and long-term stability has been a long-sought goal. Unfortunately, copper (Cu) catalysts for producing valuable multicarbon (C 2+ ) products undergo structural and morphological changes under ECR conditions, especially at high current densities, resulting in a rapid decrease in product selectivity. Herein, we report a catalyst regeneration strategy, one that employs an electrolysis method comprising alternating “on” and “off” operating regimes, to increase the operating stability of a Cu catalyst. We find that it increases operating lifetime many times, maintaining ethylene selectivity ≥40% for at least 200 h of electrolysis in neutral pH media at a current density of 150 mA cm –2 using a flow cell. We also demonstrate ECR to ethylene at a current density of 1 A cm –2 with ethylene selectivity ≥40% using a three-dimensional Cu gas diffusion electrode, finding that this system under these conditions is rendered stable for greater than 36 h. This work illustrates that Cu-based catalysts, once they have entered into the state conventionally considered to possess degraded catalytic activity, may be recovered to deliver high C 2+ selectivity. We present evidence that the combination of short periods of electrolysis, which minimizes the morphological changes during “on” segments, with the progressive chemical oxidation of Cu atoms on the catalyst surface during “off” segments, united with the added effects of washing the accumulated salt and decreasing the catholyte temperature prolong together the catalyst’s operating lifetime.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".