Electrocatalysts Derived from Copper Complexes Transform CO into C <sub>2+</sub> Products Effectively in a Flow Cell
Bibliographic record
Abstract
Abstract Electrochemical reactors that electrolytically convert CO 2 into higher‐value chemicals and fuels often pass a concentrated hydroxide electrolyte across the cathode. This strongly alkaline medium converts the majority of CO 2 into unreactive HCO 3 − and CO 3 2− byproducts rather than into CO 2 reduction reaction (CO2RR) products. The electrolysis of CO (instead of CO 2 ) does not suffer from this undesirable reaction chemistry because CO does not react with OH − . Moreover, CO can be more readily reduced into products containing two or more carbon atoms (i. e., C 2+ products) compared to CO 2 . We demonstrate here that an electrocatalyst layer derived from copper phthalocyanine ( CuPc ) mediates this conversion effectively in a flow cell. This catalyst achieved a 25 % higher selectivity for acetate formation at 200 mA/cm 2 than a known state‐of‐art oxide‐derived Cu catalyst tested in the same flow cell. A gas diffusion electrode coated with CuPc electrolyzed CO into C 2+ products at high rates of product formation (i. e., current densities ≥200 mA/cm 2 ), and at high faradaic efficiencies for C 2+ production ( FE C2+ ; >70 % at 200 mA/cm 2 ). While operando Raman spectroscopy did not reveal evidence of structural changes to the copper molecular complex, X‐ray photoelectron spectroscopy suggests that the catalyst undergoes conversion to a metallic copper species during catalysis. Notwithstanding, the ligand environment about the metal still impacts catalysis, which we demonstrated through the study of a homologous CuPc bearing ethoxy substituents. These findings reveal new strategies for using metal complexes for the formation of carbon‐neutral chemicals and fuels at industrially relevant conditions.
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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".