CO<sub>2</sub> Electroreduction on Different Mono- and Bi-Metallic Electrocatalysts: Synthesis, Characterization and Electrode Design
Bibliographic record
Abstract
It is well known that CO2 is a major green-house gas with significant influence on increase of overall global temperature. Recent work on CO2, however, has shown that it can be used as feedstock for production of value-added products such as alcohols, formate, CO, and methane or ethane [1]. Electrochemical conversion is one way of utilizing CO2 with the advantage of easier scale-up and operation under ambient temperature and pressure. This technology is currently in the developmental stage and can therefore benefit from the knowledge obtained from research in the field of the proton exchange membrane fuel cell (PEMFC) or even anion exchange membrane fuel cells (AEMFCs). Similar to the oxygen reduction reaction (ORR) in PEMFCs/AEMFCs, the reaction at the cathode of a CO2 electrolyzer requires an engineered electrocatalyst. Furthermore, it should be mentioned that contrary to the ORR, CO2 electroreduction reaction results in multiple reaction products that are generated both in liquid and gas phase and the hydrogen evolution reaction (HER) is a competing reaction. In order to make the CO2 electroreduction systems viable and prevent cost-heavy separation of products, electrocatalysts with high selectivity towards the desired product should be designed, synthesized, and scaled-up. Herein we report our recent results on synthetic development method – based on Sacrificial Support Method (SSM) for preparation of mono- and bi-metallic materials [2-5]. The method of the evaluation of electrocatalytic activity of un-supported catalysts for the CO2 electroreduction reaction was based on the rotating disk electrode (RDE) technique and online gas chromatography (GC). The sealed RDE cell was specially designed at Naval Research Laboratory and it was demonstrated that the reaction products generated on small surface area thin RDE films can be quantified by online GC. Liquid reaction products are separated and identified ex-situ by liquid chromatography or NMR (experiments performed at UNM). Figure 1 shows SEM morphology of copper-based electrocatalysts, XRD data and electrochemical performance from RDE experiments. It was shown that, by controlling the SSM parameters, it was possible to synthesize electrocatalysts selective to one product only (except hydrogen). Acknowledgements OAB is grateful to the Office of Naval Research for financial support of this project. References [1] Y. Hori, Electrochemical CO2 reduction on metal electrodes, in: C.e.a. Vayenas (Ed.) Modern Aspects of Electrochemistry, vol. 42, Springer, New York, 2008. [2] A. Serov, K. Artyushkova, N. I. Andersen, S. Stariha, P. Atanassov "Original Mechanochemical Synthesis of Non-Platinum Group Metals Oxygen Reduction Reaction Catalysts Assisted by Sacrificial Support Method", Electrochim. Acta (2015) doi:10.1016/j.electacta.2015.02.108 [3] A. Serov, N. I. Andersen, A. J. Roy, I. Matanovic, K. Artyushkova, P. Atanassov, “CuCo2O4 ORR/OER Bi-Functional Catalyst: Influence of Synthetic Approach on Performance”, J. of The Electrochem. Soc., 162 (4) (2015) F449-F454 [4] C. Santoro, A. Serov, C. W. Narvaez Villarrubia, S. Stariha, S. Babanova, A. J. Schuler, K. Artyushkova, P. Atanassov. “Double‐Chamber Microbial Fuel Cell with a Non‐Platinum‐Group Metal Fe–N–C Cathode Catalyst”, ChemSusChem, 8 (2015), 828-834. [5] N. I. Andersen, A. Serov, P. Atanassov “Metal Oxides/CNT Nano-Composite Catalysts for Oxygen Reduction/Oxygen Evolution in Alkaline Media”, Appl. Catal. B: Environmental, 163 (2015), 623-627. [6] Z. Zhang, K.L. More, K. Sun, Z. Wu, W. Li, Chemistry of Materials, 23 (2011) 1570. [7] S. Trasatti, O.A. Petrii, Pure and Applied Chemistry, 63 (1991) 711. 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".