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 (CO 2 ) to ethylene (C 2 H 4 ) in the CO 2 reduction reaction (CO 2 RR) requires fine control over the phase boundary of the gas diffusion electrode (GDE) to overcome the limit of CO 2 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 C 2 H 4 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 CO 2 concentration near the active sites of the Cu catalysts. MOFs with different CO 2 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% C 2 H 4 Faradaic efficiency (FE) at a current density of 200 mA cm − 2 in a flow cell, 49% C 2 H 4 FE at 1 A cm − 2 is achieved on MOF‐augmented GDEs in CO 2 RR. MOF‐augmented GDEs are further evaluated in an MEA electrolyzer, achieving a C 2 H 4 partial current density of 220 mA cm −2 for CO 2 RR and 121 mA cm −2 for the carbon monoxide reduction reaction (CORR), representing 2.7‐fold and 15‐fold improvement in C 2 H 4 production rate, compared to those obtained on bare Cu/PTFE.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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 teacher head, 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".