High‐Rate and Selective CO<sub>2</sub> Electrolysis to Ethylene via Metal–Organic‐Framework‐Augmented CO<sub>2</sub> Availability
Bibliographic record
Abstract
Abstract High‐rate conversion of carbon dioxide (CO2) to ethylene (C2H4) in the CO2 reduction reaction (CO2RR) requires fine control over the phase boundary of the gas diffusion electrode (GDE) to overcome the limit of CO2 solubility in aqueous electrolytes. Here, a metal–organic framework (MOF)‐functionalized GDE design is presented, based on a catalysts:MOFs:hydrophobic substrate materials layered architecture, that leads to high‐rate and selective C2H4 production in flow cells and membrane electrode assembly (MEA) electrolyzers. It is found that using electroanalysis and operando X‐ray absorption spectroscopy (XAS), MOF‐induced organic layers in GDEs augment the local CO2 concentration near the active sites of the Cu catalysts. MOFs with different CO2 adsorption abilities are used, and the stacking ordering of MOFs in the GDE is varied. While sputtering Cu on poly(tetrafluoroethylene) (PTFE) (Cu/PTFE) exhibits 43% C2H4 Faradaic efficiency (FE) at a current density of 200 mA cm−2 in a flow cell, 49% C2H4 FE at 1 A cm−2 is achieved on MOF‐augmented GDEs in CO2RR. MOF‐augmented GDEs are further evaluated in an MEA electrolyzer, achieving a C2H4 partial current density of 220 mA cm−2 for CO2RR and 121 mA cm−2 for the carbon monoxide reduction reaction (CORR), representing 2.7‐fold and 15‐fold improvement in C2H4 production rate, compared to those obtained on bare Cu/PTFE.
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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".