Permselective MOF-Based Gas Diffusion Electrode for Direct Conversion of CO<sub>2</sub> from Quasi Flue Gas
Bibliographic record
Abstract
Industrial activities lead to a substantial share of current anthropogenic CO 2 emissions and are some of the most challenging to abate. Direct utilization of industrial flue gases to produce fuels or value-added chemicals is challenging due to the presence of impurities and low concentrations of CO 2 . Herein, we demonstrate a rational assembly of a permselective gas diffusion electrode (PGDE) for direct CO 2 conversion from quasi flue gas (i.e., 10–15% CO 2, 4% O 2, and N 2 balance at 100% relative humidity). The electrode design consists of a metal–organic framework (MOF) based mixed matrix membrane (MMM) that enables the selective permeation of CO 2 to a silver electrocatalyst. The MOF is CALF-20, notable for the ability to physisorb CO 2 in wet gas streams. Applying this approach, we convert N 2 -diluted CO 2 streams to CO at a faradaic efficiency of 95% compared to 58% for the nonmodified counterpart electrode with MMM. The PGDE retained its electrochemical performance when introducing O 2 by preventing ∼84% loss of current toward parasitic oxygen reduction reaction (ORR) and reported 30 mA cm –2 CO partial current density. Further, wetting the gas stream showed a negligible effect on the MOF and the electrochemical performance. Using our PDGE, we report nearly constant CO selectivity over 19 h in a membrane electrode assembly electrolyzer. This approach offers the potential for direct utilization of low-concentration CO 2 while avoiding the economic and environmental costs of obtaining purified CO 2 feedstocks.
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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 | 0.001 |
| 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".