Reactive Molecular Dynamics Study for CO2 Electrocatalytic Conversion on Lmfcr (M= Sr, Ca) Perovskite-Based Solid Oxides
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».