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Enregistrement W4285398259 · doi:10.1149/ma2022-01391774mtgabs

Transition Metal Doping of 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> for Nanoparticle-Enhanced Reversible CO<sub>2</sub>-CO Electrocatalysis

2022· article· en· W4285398259 sur OpenAlexaff
Haris Masood Ansari, Sara Bouzidi, Adam J. Bass, Viola Birss

Notice bibliographique

RevueECS Meeting Abstracts · 2022
Typearticle
Langueen
DomaineMaterials Science
ThématiqueAdvancements in Solid Oxide Fuel Cells
Établissements canadiensUniversity of Calgary
Organismes subventionnairesnon disponible
Mots-clésOxideOxidizing agentMaterials scienceElectrolysisDopingChemical engineeringCatalysisElectrolyteYttria-stabilized zirconiaEnergy storageTransition metalSolid oxide fuel cellNanoparticlePerovskite (structure)ElectrodeInorganic chemistryNanotechnologyCubic zirconiaCeramicChemistryOptoelectronicsPhysical chemistryMetallurgyThermodynamics

Résumé

récupéré en direct d'OpenAlex

Fluctuations in supply and demand result in wasted energy during off-peak electric grid hours, with Reversible Solid Oxide Cells (RSOCs) potentially providing an efficient means by which such energy can be stored to create a highly sustainable grid.1 A RSOC can catalyze reactions such as the CO2 Reduction Reaction (CO2RR) by operating in the Solid Oxide Electrolysis Cell (SOEC) mode when energy supply is high, or in the Solid Oxide Fuel Cell (SOFC) mode when demand is high while oxidizing CO.1 The estimated yearly energy storage requirement is about 2-3 months per year, with RSOCs capable of achieving this goal.1 Single phase perovskite materials with the composition La0.3M0.7Fe0.7Cr0.3O3-δ (M = Sr, Ca) (LMFCr) have emerged as promising electrocatalysts for both fuel and oxygen electrodes in symmetrical RSOCs due to their phase stability in air and fuels (pO2 ~ 0.21 – 10-21 atm) and their compatibility with doped ceria-buffered yttria- and scandia-stabilized zirconia (YSZ/SSZ) electrolytes.2,3 The Ca analogue of LMFCr (LCFCr) has generally shown better performance and compatibility with other cell components,3 with significant efforts directed towards further enhancing its CO2RR activity by employing various approaches, including nanoscale modifications.4 One such technique is nanoparticle (NP) exsolution, which can greatly increase the catalytic surface area.5 This method employs B-site doping with Ni or Co followed by exposure to reducing conditions to decorate the catalyst surface with B-site metal alloy NPs.4 These are strongly anchored to the bulk material, highly resistant to coking, and stable under the harsh conditions of RSOCs.4 In our initial work, the exsolution characteristics of Fe-Ni NPs from 5% Ni-doped LCFCr (LCFCrN) were studied in H2:N2 and CO2:CO atmospheres using ex situ XRD, SEM, and STEM-EDS.4 Exsolution kinetics were rapid in H2:N2 (pO2 ~ 10-23 atm) at 800 °C with NPs assuming an average size of 45 nm and an Fe-rich Fe0.64Ni0.36 composition within 1 h exposure to 5H2:95N2. On the other hand, 70CO:30CO2 atmospheres (pO2 ~ 10-20 atm) gave sluggish kinetics with the NPs achieving an average size of 40 nm and a Ni-rich FeNi3 composition even after more than 25 hours of treatment. This suggests stability to coarsening in highly reducing atmospheres.4 In more recent work, 5% Co-doped LCFCr (LCFCrCo) perovskites show visible Fe-Co NP formation at 800 °C upon exposure to 5H2:95N2 for at least 1 h or to 70CO:30CO2 for at least 5 h. Electrochemical characterization of Fe-Ni NP decorated LCFCrN (Fe-Ni@LCFCrN) was conducted on 2.5 cm diameter cells using ceria-buffered SSZ electrolyte. A LCFCrN ink was screen-printed onto a ~0.5 cm2 area on both sides of the cell followed by sintering at 1100 °C for 2 h. Au was painted onto each electrode, and the cell was sintered again at 825 °C for 1 h. Electrical connections were made via Au gauzes and wires. NP exsolution was induced by exposing the fuel electrode to 5H2:95N2 for 2 h at 800 °C. Various CO2:CO mixtures (100:0, 90:10, 70:30, 50:50) were supplied to the fuel electrode while air was supplied to the oxygen electrode with flow rates of 50 mL/min. Electrochemical performance was tested via cyclic voltammetry (CV), electrochemical impedance spectroscopy (EIS), and chronoamperometry. CVs showed that the NPs enhanced LCFCrN activity for CO2RR (~ 15%) and more notably for CO oxidation (~ 75%) at the same overpotential (~ 0.7 V), making the catalyst equally active for the two reactions. 15-minute potentiostatic tests indicated stable current densities of about –0.65, –0.634, and –0.618 A/cm2 in 100% CO2, 90CO2:10CO, and 70CO2:30CO respectively at a cell potential of 1.6 V. Medium-term (10 h) potentiostatic tests for CO2RR indicated excellent stability with a current density of –0.28 A/cm2 at a cell potential of 1.3 V in 70CO2:30CO (pO2 ~ 10-18 atm). The excellent electrochemical performance in both the SOFC and SOEC modes makes Fe-Ni@LCFCrN a very promising electrode material for RSOCs. Further work on LCFCrCo is underway, with comparisons being made between LCFCrN and LCFCrCo on CO2-CO electrocatalysis and NP characteristics. References Jensen, S. H.; Graves, C.; Mogensen, M.; Wendel, C.; Braun, R.; Hughes, G.; Gao, Z.; Barnett, S. A. Energy Environ. Sci. 8, 2471 (2015). Molero-Sánchez, B.; Addo, P.; Buyukaksoy, A.; Paulson, S.; Birss, V. I. Faraday Discuss. 182, 159 (2015). Molero-Sánchez, B.; Prado-Gonjal, J.; Avila-Brande, D.; Chen, M.; Moran, E.; Birss, V. I. J. Hydrog. Energy. 40, 1902 (2015). Ansari, H. M.; Bass, A. S.; Ahmad, N.; Birss, V. I. Mater. Chem. A (2022). Zhu, Y.; Dai, J.; Zhou, W.; Zhong, Y.; Wang, H.; Shao, Z. Mater. Chem. A. 6, 13582 (2018).

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: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Autre · Signal consensuel: aucune
Score de désaccord entre enseignants0,002
Score d'incertitude au seuil0,006

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)0,0020,001

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 Opus0,013
Tête enseignante GPT0,253
Écart entre enseignants0,240 · 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'étudeSans objet
Domainenon disponible
GenreAutre

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
Publié2022
Routes d'admission1
Résumé présentoui

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