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Enregistrement W4391663310 · doi:10.1149/ma2023-02462238mtgabs

Minimizing Coke Formation at 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> Perovskite Anodes in a Syngas Fed-SOFC

2023· article· en· W4391663310 sur OpenAlexaff
Adam Bass, Anand Chandra Singh, Scott Paulson, Viola Birss

Notice bibliographique

RevueECS Meeting Abstracts · 2023
Typearticle
Langueen
DomaineMaterials Science
ThématiqueAdvancements in Solid Oxide Fuel Cells
Établissements canadiensUniversity of Calgary
Organismes subventionnairesnon disponible
Mots-clésPerovskite (structure)Materials scienceCokeAnodeMineralogyAnalytical Chemistry (journal)MetallurgyCrystallographyChemistryPhysical chemistryElectrodeEnvironmental chemistry

Résumé

récupéré en direct d'OpenAlex

As the world moves to decarbonize the fossil fuel sector, transition technologies are needed that bridge the gap between natural gas power plants and more sustainable low-carbon energy sources. These newer technologies often still rely on fossil fuels but have improved energy conversion efficiencies and lower net carbon dioxide (CO2) outputs over conventional fossil fuel based electric power generation systems. In this work, we are exploring one such technology, namely the use of a syngas-fed solid oxide fuel cell (SOFC) to generate heat, electricity, steam, and captured CO2. Core to this technology is the mixed ion electron conductor deployed at the anode and cathode that catalyzes all of the relevant reactions, namely electrochemical oxidation of hydrogen (H2) and carbon monoxide (CO) at the anode, producing steam and CO2, and reduction of oxygen at the cathode. Carbon formation (coking) is normally a significant problem affecting SOFCs operating on carbon-based fuels, as it leads to a rapid decline in electrochemical performance by blocking catalytically active sites and pores with various carbon species, e.g., amorphous, graphitic, or nanotubular carbon.1 The formation of carbon species from syngas is known to occur through various mechanisms, with the Boudouard reaction (∆H= -172 kJ/mol) and the reduction of CO (∆H= -131 kJ/mol) being the most prominent.2 As such, temperature is a key parameter to optimize as it determines the propensity for carbon formation at equilibrium. In addition, the kinetics of carbon formation can be significantly reduced by introducing oxygen to the fuel gas stream in the form of O2, CO2, or H2O.3 The catalyst materials investigated here are mixed conducting perovskite oxides (La0.3Ca0.7Fe0.7Cr0.3O3- δ, LCFCr) that have been optimized and modified recently by our group, both in the as-prepared undoped form and after B-site doping with variable quantities of transition metals (M), e.g., Ni,4 forming nanoparticle (NP)-decorated ABO3-Mx surfaces. Our catalyst is highly active for H2 and CO oxidation, CO2 reduction, and O2 reduction, where it was demonstrated that the un-doped parent material can deliver a stable power density of 0.2 W/cm2 for several hundred hours with negligible performance degradation in 3% humidified H2.5 In more recent work, excellent resilience to carbon deposition for exsolved Fe-Ni@LCFCr up to 70:30 CO:CO2 was demonstrated.4 Herein, we show that minimal coke forms during exposure of these materials to dry syngas at 600oC, even under open circuit conditions. The catalysts were prepared using combustion synthesis and were characterized by XRD, SEM EDX, and TPO-MS in order to confirm morphology, crystal structure, and composition as a function of temperature and gas environment.4 Symmetrical electrolyte-supported SOFCs were constructed using our catalyst as both the anode and cathode. Catalyst layers of 1 cm2 were screen printed to a thickness of 25 µm on both sides of commercially available 130 µm thick samaria-doped ceria (SDC)-buffered scandia-stabilized zirconia (ScSZ) electrolyte, followed by sintering at 1100°C for 2 h,4 with porous metal current collectors used. The cells were mounted and tested in a Fiaxel SOFC test station with gas flow controlled by mass flow controllers. Preliminary electrochemistry experiments were conducted in 5:95 H2:N2, or 1:1 H2:CO (syngas) balanced by CO2 in a 1:2 ratio of fuel to oxidant into the anode chamber, and air into the cathode chamber at 600 oC, with performance evaluation carried out using CV, EIS and chronopotentiometry. The power density was found to be ca. 2x higher in dry H2 vs. in syngas, as expected, considering that H2 is a more active fuel vs. CO. Additionally, EIS exhibited ca. 2x higher resistance in the low frequency arc in syngas, which can be attributed to sluggish CO oxidation kinetics.4 Chronopotentiometry was performed for 20 h at 10 mA cm-2, showing a degradation rate of only 0.08 mV h-1, suspected to be primarily due to current collector delamination. Coking studies were also conducted on button cells at 600 oC in 1:1 H2:CO for 25 h at open circuit, comparing to a NiO standard that was painted on the electrolyte just next to the LCFCr-Ni working electrode. Imaging by SEM showed negligible carbon formation on the perovskite surface, supported by EDX analysis, compared to the extensive degree of coking observed at the standard. Further quantification was conducted by TPO-MS, also confirming minimal carbon formation. References Bengaard et al., Journal of Catalysis, 2002, 209, 365–384. Farshchi Tabrizi et al., Energy Conversion and Management, 2015, 103, 1065–1077. Sasaki et al., Journal of The Electrochemical Society, 2003, 150. Ansari et al., Journal of Materials Chemistry A, 2022, 10, 2280–2294. Addo et al., ECS Transactions, 2015, 66, 219–228.

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 enseignants0,001
Score d'incertitude au seuil0,002

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)0,0010,000

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.

Tête enseignante Opus0,019
Tête enseignante GPT0,255
Écart entre enseignants0,236 · 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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