CO<sub>2</sub> Reduction at the Triphasic Interface: Enhancing Ethylene Production 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 lessen our impact on our environment. The CO2 reduction reaction (CO2RR) offers a synthetic route to a number of key materials, such as methane,[1] ethylene,[2] formate[3] and carbon monoxide.[4] This provides a two-fold environmental benefit, since CO2 could be captured from industrial processes rather than being released into the environment, and then used to produce a material that would usually be sourced from fossil fuels. Much work has been dedicated to the CO2RR at copper electrodes thanks to its ability to produce C2 species such ethylene with reasonable selectivity. Additional advances come from using gas diffusion electrodes (GDEs), which circumvents issues around the low solubility of CO2 in aqueous electrolytes. However, the currently attainable selectivity is not yet sufficient for practical applications. The outflow from CO2RR reactors contains mixtures of a number of possible CO2RR products, along with a substantial amount of H2 formed by water reduction at the same applied potentials. Here, we improve the selectivity of copper GDEs towards ethylene using Polymers with Intrinsic Microporosity (PIMs). These PIMs can be easily drop-cast onto the GDE surface, forming a microporous layer at the catalyst – electrolyte interface. The microporous structure stores gases in a triphasic interface at the electrode surface,[5] which has previously been shown to improve catalyst activity towards oxygen reduction.[6] We show that the introduction of a PIMs triphasic interface to copper GDEs substantially improves the performance of the CO2RR towards ethylene. This is evidenced by an increased Faradaic efficiency, increased GDE stability and shift in the reduction wave to lower overpotentials. The impact of the PIMs is significantly dependent on the loading at the catalyst surface, with thin PIMs layers enhancing performance, but thicker PIMs layers having a surprisingly detrimental effect. This work acts as a proof of concept, demonstrating that triphasic interfaces can enhance the activity of GDEs for CO2RR towards ethylene production. References: 1. Y. Hori, H. Wakebe, T. Tsukamoto, O. Koga, Surf. Sci. 1995, 335, 258-263. 2. C.-T. Dinh, T. Burdyny, M. G. Kibria, A. Seifitokaldani, C. M. Gabardo, F. P. García de Arquer, A. Kiani, J. P. Edwards, P. De Luna, O. S. Bushuyev, C. Zou, R. Quintero-Bermudez, Y. Pang, D. Sinton, E. H. Sargent, Science 2018, 360, 783-787. 3. S. Gao, Y. Lin, X. Jiao, Y. Sun, Q. Luo, W. Zhang, D. Li, J. Yang, Y. Xie, Nature 2016, 529, 68. 4. J. L. DiMeglio, J. Rosenthal, J. Am. Chem. Soc. 2013, 135, 8798-8801. 5. F. Marken, E. Madrid, Y. Zhao, M. Carta, N. B. McKeown, ChemElectroChem 2019, 6, 1-12. 6. E. Madrid, J. P. Lowe, K. J. Msayib, N. B. McKeown, Q. Song, G. A. Attard, T. Düren, F. Marken, ChemElectroChem 2019, 6, 252-259. 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 | 0.000 |
| 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".