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Record W3025195370 · doi:10.1149/ma2020-01371566mtgabs

The Role of Activation Process on Perovskites-Type Oxides As Electrocatalysts for Oxygen Evolution Reaction

2020· article· en· W3025195370 on OpenAlexaff
José G. Rivera, Francesca Deganello, G. Orozco, Ana C. Tavares

Bibliographic record

VenueECS Meeting Abstracts · 2020
Typearticle
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsOverpotentialOxygen evolutionElectrocatalystOxidePerovskite (structure)Materials scienceElectrochemistryCatalysisInorganic chemistryPolarization (electrochemistry)Chemical engineeringCyclic voltammetryChemistryElectrodePhysical chemistryMetallurgyOrganic chemistry

Abstract

fetched live from OpenAlex

Oxygen evolution reaction (OER) is one of the most extensively studied electrochemical reactions. This reaction is of paramount importance in the development of electrochemical technologies such metal-air batteries and electrolyzers. OER is thermodynamically unfavorable and a large overpotential is required to drive the reaction at a rate of practical interest 1 . Therefore, the development of efficient, durable and low-cost electrocatalysts is necessary. Perovskite-type oxides of the general formula ABO 3 are one of the most promising class of materials for this application 2 . The perovskite structure is able to accommodate a large variety of dopant ions which provides high flexibility in shaping their physical-chemical properties and catalytic activity 3,4 . The OER activity of perovskite catalysts is usually evaluated in the form of a composite thin film (carbon black + perovskite + binder) on a rotating disk electrode (RDE). Carbon is added to increase the conductivity of the catalyst layer, but it can also promote the OER activity of the oxide through a synergic effect 5 . However, a wide variety of formulations and oxide loading have been used, making it difficult to compare results between works. The protocol to evaluate the OER activity for an electrocatalyst usually involves: i) conditioning of the electrode by cyclic voltammetry, ii) determination of the electrochemical surface area (ECSA) and iii) recording the OER polarization curves 6,7 . However, in the literature there is lack of information about the influence of the history of the electrodes on the OER activity of the perovskite oxide catalysts 7 . Here, we present a specific study on the OER activity of La 0.5 Sr 0.5 Co 0.8 Fe 0.2 O 3- δ perovskite electrocatalyst prepared by solution combustion synthesis, in which the effect of the conditioning of the thin film electrode on the activity towards OER are evaluated. Figure 1 shows the OER polarization curves for a fresh electrode (FE) and for an activated electrode (AE). Compared with FE, the AE shows a better wettability by the electrolyte, higher voltammetric charge under the peaks associated with the Co 3+ species and an increase of the OER current at 1.7 V by 117%. The Co 3+ ions occupy the B-sites of the perovskite structure which are the active sites for the OER. The conditions and reasons leading to more active sites, resulting in higher current densities will be discussed in detail. Figure 1. Polarization curves of a fresh and activated electrode in 0.1 M KOH at 0.5 mVs -1 and 1600 rpm. Inset: cyclic voltammograms showing the presence of cobalt redox peaks between 1.0 and 1.4 V vs RHE. The enhancement on the OER activity and wettability of the electrode is correlated with a higher voltammetric charge under the redox peaks. References 1. I. Katsounaros, S. Cherevko, A. R. Zeradjanin, and K. J. J. Mayrhofer, Angew. Chemie - Int. Ed. , 53 , 102–121 (2014). 2. S. Gupta et al., Chem. - An Asian J. , 11 , 10–21 (2016). 3. J. Suntivich et al., Nat. Chem. , 3 , 546–550 (2011). 4. J. Suntivich, K. J. May, H. A. Gasteiger, J. B. Goodenough, and Y. Shao-Horn, Science (80-. ). , 334 , 1383–1385 (2011). 5. R. Mohamed et al., J. Electrochem. Soc. , 162 , F579–F586 (2015). 6. F. Deganello et al., ACS Appl. Energy Mater. , 1 , 2565–2575 (2018). 7. G. Li, L. Anderson, Y. Chen, M. Pan, and P. Y. Abel Chuang, Sustain. Energy Fuels , 2 , 237–251 (2018). Figure 1

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How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation 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 score0.034
Threshold uncertainty score0.729

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
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)0.0000.000

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 head0.010
GPT teacher head0.236
Teacher spread0.226 · 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 teacher head, 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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Citations1
Published2020
Admission routes1
Has abstractyes

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