Investigation of Electrocatalytic CO<sub>2</sub> Reduction Reaction Mechanism: Role of H<sub>2</sub>s and Exsolved Metal Nanoparticle at Perovskite Oxides Electrodes
Bibliographic record
Abstract
ABSTRACT Affordable and sustainable energy demands from post-modern society as well as the need to lower the CO2 emissions require alternative approaches rather than business-as-usual [1]. Solid oxide fuel cells (SOFC) and solid oxide electrolysis cells (SOEC) are one of the promising technologies that are being developed to meet these needs. The working principle of an SOEC is based on heterogeneous electrocatalysis to produce syngas (CO and H2) by splitting CO2 and H2O at high temperatures. The stability and electrochemical performance of the components, including the solid-state electrolyte and the electrode materials, are vital to making this technology mature enough to enhance its economic competitiveness [2]. Therefore, the physical and chemical features of the components need to be understood fundamentally. Due to its high electrocatalytic activity, Ni-YSZ cermets are still the most commonly used electrode materials in SOECs. However, the poisoning of these catalysts by sulfur deposition and coke formation is a problem. As a result, there has been growing interest in using mixed conducting perovskites oxides (MIECS) as electrode materials, which are more resistant to these challenges and as their chemical and physical properties can be easily tailored [3]. Here, we investigate a family of MIECS that can be used in many fuel environments and is also highly active in air, namely LCFCr (La0.3Ca0.7Fe0.7Cr0.3O3-δ). One specific objective of the current study is to elucidate the CO2 reduction mechanism based on varying the composition of LCFCr [4,5] as well as by alterations of the operating conditions, such as temperature, reactant gas mixture, and applied potential. Importantly, the effect of the presence of H2S in the gas phase [6] and ex-solved metal nanoparticles on the surface [7] during exposure to CO2 at open circuit and under CO2 reduction conditions is being investigated. This work involves the use of reactive molecular dynamics (ReaxFF MD), electrochemical perturbations, and thermo-catalytic methods, with our initial ReaxFF MD results showing good agreement with the experimental results [8]. ACKNOWLEDGMENTS This computational research was supported by the Canada First Research Excellence Fund (CFREF), while the platforms for this computational work were provided by Westgrid (https://www.westgrid.ca) and Compute Canada (https://www.computecanada.ca). Thanks are also extended to Dr. Haris Ansari, Oliver Calderon, Michael Pidburtnyi, Dr. Scott Paulson, Dr. Anand Singh, Sara Bouzidi and Adam Bass for helpful discussions.
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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 | 0.000 |
| 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".