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Enregistrement W4391639017 · doi:10.1149/ma2023-02462264mtgabs

(Invited) Understanding the Effects of Metal Nanoparticle Exsolution from La<sub>0.3</sub>Ca<sub>0.7</sub>Fe<sub>0.7</sub>Cr<sub>0.3</sub>O<sub>3-δ</sub> Perovskites on CO<sub>2</sub>-CO Electrocatalysis

2023· article· en· W4391639017 sur OpenAlexaff
Sara Bouzidi, Haris Masood Ansari, Adam Bass, Viola Birss

Notice bibliographique

RevueECS Meeting Abstracts · 2023
Typearticle
Langueen
DomaineMaterials Science
ThématiqueCatalytic Processes in Materials Science
Établissements canadiensUniversity of Calgary
Organismes subventionnairesnon disponible
Mots-clésMaterials scienceMetalNanoparticleAnalytical Chemistry (journal)MineralogyChemistryMetallurgyNanotechnologyEnvironmental chemistry

Résumé

récupéré en direct d'OpenAlex

Solid Oxide Fuel Cells (SOFCs) and Solid Oxide Electrolysis Cells (SOECs) are highly useful devices, capable of generating and storing large amounts of energy, respectively.1 They can do this by catalyzing CO oxidation at a SOFC anode and CO2 reduction (CO2RR) at a SOEC cathode, while the other electrode catalyzes oxygen reduction or evolution. The conduction of oxide ions through the electrolyte completes the circuit, making Solid Oxide Cells (SOCs) an excellent choice for clean energy production and use since there are no undesired byproducts.1 While traditional SOC electrodes are composed of oxide-conducting ceramics mixed with electronically conducting metals,1 a newer category of catalysts are Mixed Ionic Electronic Conductors (MIECs), with one example being the perovskite La0.3M0.7Fe0.7Cr0.3O3-δ (M = Sr, Ca) (LMFCr), investigated heavily by our group.2,3,4 As an MIEC, the full surface of LMFCr is electrochemically active, giving excellent activity at both the cathode and anode, including for CO2RR and CO oxidation.2,3 However, efforts are being made to further improve the CO2RR-CO oxidation kinetics and durability, with the Ca analogue having a better chemical match with standard electrolytes.4 One approach used recently to improve MIEC oxide performance is B-site doping with transition metals (TMs) while also creating an A-site deficiency, resulting in nanoparticle (NP) formation (exsolution) when the perovskite is subjected to reducing conditions.2 For instance, Fe-Ni NPs of a ~20 nm average size can be exsolved from (La0.3Ca0.7)0.95Fe0.7Cr0.25Ni0.05O3-δ(LCFCrNi), even at 600 °C in 70CO2:30CO (pO2 ~10-18 atm). Furthermore, NP features can easily be tailored by changing the reducing conditions or dopant used.2 In general, higher temperatures and lower pO2 lead to larger NPs over time, and more easily reducible metals tend to exsolve first under less harshly reducing conditions.2 Recent studies have suggested that NP formation enhances electrocatalytic activity by creating additional sites of reactivity, suggesting that strong NP-substrate interactions are important to catalysis.5 However, there is no clear understanding of the role played by exsolved NPs in catalyzing SOC reactions. To gain further insights, detailed electrochemical studies of LCFCrNi electrodes were done on 1-inch diameter cells constructed using Samarium-Doped Ceria buffered Scandia-Stabilized Zirconia electrolyte substrates. LCFCrNi was made into an ink and tape-cast on 0.5 cm2 on one side (working electrode, WE) and 1 cm2 on the other (counter electrode, CE) on each substrate. The cells were sintered at 1100 °C for 2 h, coated with Au ink, and sintered at 850 °C for 1 h. Electrochemical Impedance Spectroscopy (EIS) was performed at 600 °C with 70CO2:30CO at the WE and air at the CE, followed by exsolution in 5H2:95N2 at a higher temperature, and then EIS was repeated multiple times under the same conditions at 600 °C. Cells were imaged via Scanning Electron Microscopy (SEM) after ramping down to room temperature in N2 to determine the size and distribution of the NPs for each set of exsolution conditions. Under all conditions employed, the polarization resistance (Rp) was found to be stable before exsolution occurred, but started decreasing once the NPs formed, especially via a shrinking of the low frequency resistance. The low frequency resistance has been associated with CO oxidation,2 so the fact that it improves with exsolution is an indication that the increased electrode area serves to improve CO oxidation more than CO2RR. When exsolution was carried out at 800 °C for 25 h, Rp decreased more rapidly than after exsolution at 800 °C for 14 h. Exsolution at 800 °C for 25 h also showed a more rapid decrease in Rp compared to 700 °C exsolution for 25 h. These results argue that the initial formation of larger NPs (formed after longer time or at a higher temperature) results in a faster increase in the active surface area of the electrode at the lower 600 °C temperature. Further experiments are being carried out to better understand whether it is the growth of NPs or new NP formation after higher temperature exsolution that is the reason behind this observation. The results of operando synchrotron studies in CO2/CO environments before and after exsolving, as well as the use of different transition metal dopants, are also being investigated for this purpose. References [1] Mogensen, Current Opinion in Electrochemistry, 2020, 21, 265-273. [2] Ansari et al., Journal of Materials Chemistry A,2022, 10, 2280–2294. [3] Addo et al., Fuel Cells, 2015, 15, 689-696. [4] Molero-Sánchez et al., International Journal of Hydrogen Energy, 2015, 40, 1902-1910. [5] Xiong, X. et al., Journal of Materials Science & Technology, 2022, 125, 51-58.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: Expérimental (laboratoire)
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,006
Score d'incertitude au seuil0,019

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0060,001

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.

Tête enseignante Opus0,017
Tête enseignante GPT0,246
Écart entre enseignants0,229 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeExpérimental (laboratoire)
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2023
Routes d'admission1
Résumé présentoui

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