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Record W4386853031 · doi:10.1149/ma2023-01442369mtgabs

Electrochemical Characterization of Pd-Based Catalysts Deposited on Yittria Stabilized Zirconia Solid Electrolyte for Methane Oxidation Reaction

2023· article· en· W4386853031 on OpenAlexaff
Najmeh Ahledel, Komalpreet Kaur Saini, Martin Couillard, Elena A. Baranova

Bibliographic record

VenueECS Meeting Abstracts · 2023
Typearticle
Languageen
FieldMaterials Science
TopicCatalytic Processes in Materials Science
Canadian institutionsNational Research Council CanadaUniversity of Ottawa
Fundersnot available
KeywordsCatalysisMethaneElectrochemistryNoble metalInorganic chemistryAnaerobic oxidation of methaneOxideElectrolyteChemistryOxidative coupling of methaneMaterials scienceChemical engineeringElectrodeOrganic chemistry

Abstract

fetched live from OpenAlex

Methane, the main constituent of natural gas, has received increased attention since it has a higher energy density than other fossil fuels while producing less particulates containing sulphur and nitrogen. However, although methane emits less CO2 than other fossil fuels, it has 28 times more global warming potential [1]. Therefore, complete methane oxidation via heterogeneous catalysis that prevents escaping unburned methane from the engine exhaust is a key solution to this issue. Palladium is the most effective conventional noble metal for methane catalytic oxidation, which is susceptible to deactivation, especially in the presence of water. The stability and activity of a monometallic Pd catalyst could be improved upon adding a second less expensive and more abundant non-noble metal or metal oxide[2]. The interaction of Pd nanoparticles and the second metal can be characterized using electrochemical means to understand the catalytic and electrocatalytic activity of catalysts[3,4]. In this work, the electrochemical behaviour of PdM (M= Co, Sn, Fe and ZnO) nanoparticles deposited on YSZ solid electrolyte was evaluated for complete methane oxidation and compared to free-standing Pd catalyst. To this end, the nanoparticles were synthesized via the polyol method and tested for their open circuit catalytic oxidation and electrochemical performance in a temperature range from 320 to 400 °C under reducing, stoichiometric and oxidizing reaction conditions. The light-off experiments in open circuit condition revealed that the presence of Sn increased the catalytic rate of the reaction more than two other metals, Co and Fe, which resulted in lowering the reaction rate per unit mass of active catalyst. Furthermore, ZnO-supported Pd demonstrated the highest catalytic reactivity due to metal-support interactions. Lastly, the results demonstrate a higher level of catalytic reactivity with higher temperatures and oxygen partial pressures (pO2). Using Tafel plot calculations from the linear sweep voltammetry measurements, the exchange current density (io) was obtained for each catalyst, as well as the apparent activation energy of the reaction in the individual condition. It was found that higher exchange current density corresponds to lower open circuit catalytic rates. The catalyst was characterized thoroughly by several physicochemical techniques, such as TEM, SEM, XRD and ICP-MS. References [1] J. A. Arminio-Ravelo and M. Escudero-Escribano, “Strategies toward the sustainable electrochemical oxidation of methane to methanol,” Curr. Opin. Green Sustain. Chem., vol. 30, p. 100489, Aug. 2021, doi: 10.1016/J.COGSC.2021.100489. [2] K. Persson, A. Ersson, K. Jansson, N. Iverlund, and S. Järås, “Influence of co-metals on bimetallic palladium catalysts for methane combustion,” J. Catal., vol. 231, no. 1, pp. 139–150, Apr. 2005, doi: 10.1016/J.JCAT.2005.01.001. [3] P. Vernoux and C. G. Vayenas, Eds., “Recent Advances in Electrochemical Promotion of Catalysis,” vol. 61, 2023, doi: 10.1007/978-3-031-13893-5. [4] H. A. E. Dole, A. C. G. S. A. Costa, M. Couillard, and E. A. Baranova, “Quantifying metal support interaction in ceria-supported Pt, PtSn and Ru nanoparticles using electrochemical technique,” J. Catal., vol. 333, pp. 40–50, Jan. 2016, doi: 10.1016/J.JCAT.2015.10.015.

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How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.017
GPT teacher head0.279
Teacher spread0.262 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2023
Admission routes1
Has abstractyes

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