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Enregistrement W4309837616 · doi:10.1149/ma2022-02502598mtgabs

Impact of Different Supports on the Performance of Ir Oxide Based Catalysts Synthesized Using Incipient Wetness Method

2022· article· en· W4309837616 sur OpenAlexaff
Himanshi Dhawan, James Woodford, Natalia Semagina, Marc Secanell

Notice bibliographique

RevueECS Meeting Abstracts · 2022
Typearticle
Langueen
DomaineEnergy
ThématiqueElectrocatalysts for Energy Conversion
Établissements canadiensUniversity of Alberta
Organismes subventionnairesnon disponible
Mots-clésCatalysisMaterials scienceOxideChemical engineeringIncipient wetness impregnationCatalyst supportMetalChemistryMetallurgyOrganic chemistry

Résumé

récupéré en direct d'OpenAlex

Development of an active and durable catalyst for oxygen evolution reaction (OER) is a pivotal part of designing an efficient PEM water electrolyser. Iridium is one of the best available catalysts because of its corrosion resistance and activity [1]. Use of Ir is however, limited by its scarcity, limited durability and high cost. Its deposition on a support is a popular way to improve the available surface area, activity and stability [2]. For applications in acidic water electrolysis, supports with the highest chemical resistance and conductivity are preferred. While different methods have been studied to impregnate a support with catalyst nanoparticles, such as Adams’s fusion method and polyol method, their scalability remains questionable due to intensive resource requirements. In this work, Ir oxide based supported catalysts have been synthesized using the incipient wetness method (IWM), one of the most commonly used method of catalyst synthesis in the industry due to its ease of scalability, limited resource requirement and the opportunity to explore the effect of strong metal support interactions. This method however has not been discussed in literature. In this study, Ir oxide (IrOx) (Ir loading on support = 20 wt. percent) was dispersed on commercial supports (ZrO2, Nb2O5, Ta2O5, ATO) using H2IrCl6.xH2O precursor (99.9% trace metal basis, Sigma Aldrich). The catalysts were calcined at 400oC for 2 h in a muffle furnace to produce Ir oxide based catalysts. Electrochemical measurements were carried out on a standard rotating-disk electrode (RDE) system (PINE Research MSR Rotator), and a three-electrode electrochemical cell in 1.0 M sulfuric acid electrolyte (H2SO4 optima grade, Fisher Scientific). The performance of the aforementioned supported Ir oxide catalyst was compared among themselves, and to benchmark commercial catalysts (Umicore Ir Black and IrOx TKK) using metrics such as mass normalized activity (A/g-1 Ir), ECSA normalized activity (mA/cm2 Ir ECSA), Tafel slope (mV/dec) and charge transfer resistance (Rct) measured during the electrochemical test. It was observed that the activity of IrOx/ZrO2 (415 A/gIr, 5.2 mA/cm2 Ir ECSA) was the best among all the 4 impregnated catalysts, and was in fact, more than an order of magnitude greater than that of IrOx/ATO (30 A/gIr, 0.3 mA/cm2 Ir ECSA) at a potential of 1.53 VRHE. A significant drop in the Tafel slope measured in the potential range of 1.45-1.55 VRHE was observed upon changing the support from ATO (80 mV/dec) to ZrO2 (60 mV/dec) hinting towards a change in the reaction mechanism. IrOx/ATO was used as a baseline due to prevalent recognition of ATO as an excellent support for OER catalysts in the literature. Upon comparison with commercial benchmark catalysts it was observed that the Ir ECSA normalized activity of IrOx/Nb2O5, IrOx/Ta2O5, IrOx/ZrO2 surpasses both Umicore Ir black ( 2.23 mA/cm2 Ir ECSA) and IrOx TKK (1.23 mA/cm2 Ir ECSA) with IrOx/ZrO2 providing the highest activity. While Yttria-stabilized zirconia (YSZ) has been a popular choice as an electrolyte and anode for high temperature SOFC due to its non-reducing nature, high thermal stability and mechanical strength, and acceptable oxygen ion conductivity [3], its application in PEM water electrolysis as catalyst support has not been discussed. In this work, we focus on finding the causes for superior performance of ZrO2 as a support for Ir oxide based OER reaction in acidic conditions through the lens of electrocatalysis. References: [1] X. Li, X. Hao, A. Abudula, and G. Guan, “Nanostructured catalysts for electrochemical water splitting: Current state and prospects,” Journal of Materials Chemistry A, vol. 4, no. 31, pp. 11973–12000, 2016, doi: 10.1039/c6ta02334g. [2] H. Dhawan, M. Secanell, and N. Semagina, “State-of-the-art iridium-based catalysts for acidic water electrolysis: a minireview of wet-chemistry synthesis methods,” Jan. 01, 2021. https://www.ingentaconnect.com/content/matthey/jmtr/pre-prints/content-jm_jmtr_semagapr21 (accessed Mar. 26, 2021). [3] T. K. Maiti et al., “Zirconia- and ceria-based electrolytes for fuel cell applications: critical advancements toward sustainable and clean energy production,” Environ Sci Pollut Res, vol. 29, no. 43, pp. 64489–64512, Sep. 2022, doi: 10.1007/s11356-022-22087-9. Figure 1

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)0,001
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 seuil0,004

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0000,001
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 savante0,0010,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,001
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 Opus0,016
Tête enseignante GPT0,263
Écart entre enseignants0,247 · 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

Citations1
Publié2022
Routes d'admission1
Résumé présentoui

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