Electrocatalysts derived from copper complexes transform CO into C2+ products effectively in a flow cell
Bibliographic record
Abstract
Abstract The highest performance flow cells capable of electrolytically converting CO2 into higher value chemicals and fuels pass a concentrated hydroxide electrolyte across the cathode. A major problem for CO2 electrolysis is that this strongly alkaline medium converts the majority of CO2 into unreactive HCO3– and CO32– rather than CO2 reduction reaction (CO2RR) products. The electrolysis of CO (instead of CO2) does not suffer from this same problem because CO does not react with hydroxide. Moreover, CO can be more readily converted into products containing two or more carbon atoms (i.e., C2+ products). While several solid-state electrocatalysts have proven competent at converting CO into C2+ products, we demonstrate here that molecular electrocatalysts are also effective at mediating this transformation in a flow cell. Using a molecular copper phthalocyanine (CuPc) electrocatalyst, CO was electrolyzed into C2+ products at high rates of product formation (i.e., current densities J ≥200 mA/cm2), and at high Faradaic efficiencies for C2+ production (FEC2+; 72% at 200 mA/cm2). These findings present a new class of electrocatalysts for making carbon-neutral chemicals and fuels.
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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".