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Record W4285398545 · doi:10.1149/ma2022-01461973mtgabs

Reactive Molecular Dynamics Study for CO2 Electrocatalytic Conversion on Lmfcr (M= Sr, Ca) Perovskite-Based Solid Oxides

2022· article· en· W4285398545 on OpenAlexaffabout
Irfan Aydogdu, Franz Michel Martinez, Haris Masood Ansari, Viola Birss, Sathish Ponnurangam

Bibliographic record

VenueECS Meeting Abstracts · 2022
Typearticle
Languageen
FieldMaterials Science
TopicAdvancements in Solid Oxide Fuel Cells
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsPerovskite (structure)Materials scienceElectrolysisOxideDopantChemical engineeringRedoxInorganic chemistryCatalysisSinteringMetalDopingChemistryMetallurgyPhysical chemistryElectrolyteElectrode

Abstract

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Due to their mixed conducting properties as well as their electronic, crystallographic, and chemical versatility, perovskite-based oxides have been shown to be very promising electrocatalysts for CO2 conversion to syngas in high temperature (600 – 1000 °C) solid oxide electrolysis cells (SOECs) integrated with renewable energy resources. In addition to generating fuels and value-added chemicals, CO2 reduction (CO2RR) in SOECs serves to lower atmospheric CO2 emissions. La-based (1) perovskite oxides have been intensively used instead of precious metals and metal-cermet materials (e.g., Ni-YSZ, Ni-GDC) for CO2 electrolysis in high temperature SOECs. The effect of the dopant cations on the electrocatalytic performance and degradation have also been investigated since the segregation of dopants can reconstruct the surface (2). Our team has been actively developing the family of La0.3a0.7Fe0.7Cr0.3O3−δ (LMFCr, where M = Ca or Sr) for CO2 reduction (3,4) showing that CO2RR is more active than CO oxidation, and that the catalyst is also highly active on the air side. More recently, we have shown that Fe-Ni exsolved LCFCr significantly improves the rate of CO oxidation (5), while hardly changed CO2RR. However, a full atomistic scale understanding of the CO2 reduction mechanism and related degradation issues, such as dynamic changes of the surface composition as a function of operating conditions, does not yet exist. The oxygen vacancy concentration (V0) and surface termination of the perovskite material are important factors that affect the CO2RR, as shown recently (6) utilizing DFT methods. In this study, we used Density Functional Theory (DFT) to explore the energetics of the catalytic conversion of CO2 to CO on LMFCr (M = Sr, Ca) perovskite surfaces. The DFT calculations were then used to generate datasets of optimized lattice structures, including point defects, equations of state, surface energies, and surface CO2/CO interactions in order to develop reactive force fields (ReaxFF). The datasets were optimized by using an evolutionary algorithm, CMA-ES, and validated in order to explore the dynamics of the catalytic conversion process under varying conditions, such as temperature, inlet CO2/CO gas ratio, the perovskite surface composition and applied electrical field. Our results have shed light on reaction steps involving adsorption of CO2 at high temperatures, C=O bond activation, and the dominant intermediates as transition states during the conversion process and their role in generating CO. These observations will serve as a foundation to determine the elementary reaction steps involved in the CO2 splitting mechanism on LMFCr at high temperatures. Furthermore, this study will enable us to determine the optimal conditions for the reaction to occur and shed light on possible surface degradation mechanisms, allowing future optimization of the stability and catalytic activity of these promising perovskites. 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 Oliver Calderon and Misha Pidburtnyi for helpful discussions. References Cao, Z., Wei, B., Miao, J., Wang, Z., Lü, Z., Li, W., Zhang, Y., Huang, X., Zhu, X., Feng, Q. and Sui, Y., (2016). Efficient electrolysis in symmetrical solid oxide electrolysis cell with highly active 3Sr0.7Fe0.7Ti0.3O3 electrode material. Electrochemistry Communications, 69, 80-83. Zhang, Y. Q., Li, J. H., Sun, Y. F., Hua, B., & Luo, J. L. (2016). Highly active and redox-stable Ce-doped LaSrCrFeO-based cathode catalyst for CO2 SOECs. ACS Applied Materials & Interfaces,, 8(10), 6457-6463. Addo, P. K., Molero‐Sanchez, B., Chen, M., Paulson, S., & Birss, V. (2015). CO/CO2 study of high performance La0. 3Sr0. 7Fe0. 7Cr0. 3O3–δ reversible SOFC electrodes. Fuel Cells, 15(5), 689-696. Molero-Sánchez, B., Morán, E., & Birss, V. (2017). Rapid and Low-Energy Fabrication of Symmetrical Solid Oxide Cells by Microwave Methods. ACS Omega, 2(7), 3716-3723. Ansari, H. M., Bass, A. S., Ahmad, N., & Birss, V. I. (2022). Unraveling the evolution of exsolved Fe–Ni alloy nanoparticles in Ni-doped La 0.3 Ca 0.7 Fe 0.7 Cr 0.3 O 3− δ and their role in enhancing CO 2–CO electrocatalysis. Journal of Materials Chemistry A. Kozokaro, V. F., Addo, P. K., Ansari, H. M., Birss, V. I., & Toroker, M. C. (2020). Optimal Oxygen Vacancy Concentration for CO2 Reduction in LSFCr Perovskite: A Combined Density Functional Theory and Thermogravimetric Analysis Measurement Study. The Journal of Physical Chemistry C, 124(50), 27453-27466.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.013
GPT teacher head0.277
Teacher spread0.263 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

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Published2022
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