Enhanced Ethylene Selectivity during CO<sub>2</sub> Reduction Using Polymers with Intrinsic Microporosity at Copper Gas Diffusion Electrodes
Bibliographic record
Abstract
CO2 reduction is a rapidly expanding area, and a key part of the global mission to reduce carbon emissions and reduce our impact on our environment. The CO2 reduction reaction (CO2RR) presents a synthetic route to a number of industrially relevant materials, such as methane, ethylene, formate and CO.1 This provides a two-fold environmental benefit; CO2 can be captured from industrial processes before being released into the environment and produce a material usually sourced from fossil fuels. Much work has been dedicated to CO2RR at copper electrodes thanks to its ability to produce C2 species such ethylene with reasonable selectivity. However, the currently attainable selectivity is not sufficient for practical applications. The outflow contains mixtures of CO2RR products, along with a substantial amount of H2 from water reduction at the same potentials. This work improves the selectivity of copper gas diffusion electrodes (GDEs) towards ethylene using Polymers with Intrinsic Microporosity (PIMs). PIMs are easily drop-cast onto the GDE, forming a microporous layer at the catalyst – electrolyte interface. The microporous structure stores gases in a triphasic interface at the electrode surface,2 which has previously been shown to improve catalyst activity towards oxygen reduction.3 We show that the introduction of a PIMs triphasic interface to copper GDEs improves the CO2RR towards ethylene, evidenced by an increased Faradaic efficiency, improved GDE stability and shift in the reduction wave to lower overpotentials.4 The impact of the PIMs is significantly dependent on the loading, with thin layers enhancing performance, but thicker layers having a surprisingly detrimental effect. This work is a proof of concept, showing the potential of triphasic interfaces for enhancing the activity of GDEs for CO2RR towards ethylene production. References: [1] S. C. Perry, et al., Curr. Opin. Electrochem., 2020, Accepted [2] F. Marken, et al., ChemElectroChem, 2019, 6, 1 [3] E. Madrid, et al., ChemElectroChem, 2019, 6, 252 [4] S. C. Perry, et al., Chemosphere, 2020, 248, 125993 Figure 1. Schematic role of PIMs at a copper GDE surface. PIMs increase both the selectivity and activity towards ethylene during CO2 reduction. Bottom right inset shows the structure of the PIM used (PIM EA-TB). Figure 1
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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) | 0.001 | 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 | 0.001 |
| 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".