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Record W3025069470 · doi:10.1149/ma2020-01462670mtgabs

Domain Size Dependence of the Oxygen Reduction Reaction on (100)-Oriented Au Thin Films

2020· article· en· W3025069470 on OpenAlexaff
Cybelle Palma de Olivera Soares, Gaëtan Buvat, Guy Denuault, Ana C. Tavares, Daniel Guay

Bibliographic record

VenueECS Meeting Abstracts · 2020
Typearticle
Languageen
FieldEngineering
TopicSemiconductor materials and devices
Canadian institutionsInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsCyclic voltammetryThin filmElectrocatalystPulsed laser depositionMaterials scienceElectrochemistryDeposition (geology)NanomaterialsNanotechnologyChemical engineeringAnalytical Chemistry (journal)ChemistryPhysical chemistryElectrodeOrganic chemistry

Abstract

fetched live from OpenAlex

Due to its importance for energy conversion and storage, the oxygen reduction reaction (ORR) is one of the most studied electrochemical reactions. It has been reported that, in alkaline media, Au(100) single crystal is the most active electrocatalyst for the ORR 1,2 . Different reasons have been proposed to explain this high activity 3,4 . Understanding this remarkable activity can guide researchers in the design of more active electrocatalysts for practical applications using nanomaterials with specific shape and size. Since the ORR is a structure-sensitive reaction 5–7 the manipulation of the structural properties of the materials is a powerful tool to elucidate the reaction mechanism responsible for the ORR and increase the activity of materials towards the reaction. In this work, (100)-oriented Au thin films were deposited on (100) MgO. Thin films with different thicknesses were prepared by pulsed laser deposition and were used as model surfaces to study the ORR activity. Pulsed Laser Deposition (PLD) is a versatile method to prepare thin films with different surface orientations (epitaxial thin films) 8,9 , 10 . The structure and microstructure of the films were assessed by X-Ray Reciprocal Space Mapping (RSM) and Atomic Force Microscopy (AFM). There results were correlated with those obtained from Cyclic Voltammetry (CV) and Sampled Current Voltammetry (SCV). The ORR activity was observed to be dependent on the film thickness, which is reflected by the shift of the onset and half-wave potentials of the ORR towards more positive potentials for the thicker films. When correlating the ORR activity with the structural and microstructural properties, Fig.1, the evidences suggest that larger lateral coherent lengths are responsible for the enhancement of the ORR catalysis on (100)-oriented Au thin films. The reasons underlying this behavior will be presented and discussed. Figure 1: Plot showing the variation of the onset and half-wave potential, and the variation of the coherence length for thin films of various thicknesses. References (1) Stamenkovic, V. R.; Strmcnik, D.; Lopes, P. P.; Markovic, N. M. Nature Materials . 2016, pp 57–69. (2) Markovic, N. M.; Tidswell, I. M.; Ross, P. N. Langmuir 1994, 10 (1), 1–4. (3) Duan, Z.; Henkelman, G. ACS Catal. 2019, 9 , 5567–5573. (4) Schneider, O. Size Dependent Electrocatalysis of Gold Nanoparticles ; Elsevier, 2018. (5) Adžić, R. R.; Marković, N. M.; Vešović, V. B.; Adzic, R. R.; Markovic, N. M.; Vesovic, V. B. J. Electroanal. Chem. 1984, 165 (1–2), 105–120. (6) Markovic, N. M.; Gasteiger, H. A.; Ross, P. N. J. Phys. Chem. 1995, 99 (11), 3411–3415. (7) Schmidt, T. J.; Stamenkovic, V.; Arenz, M.; Markovic, N. M.; Ross, P. N. Electrochim. Acta 2002, 47 (22–23), 3765–3776. (8) Sacré, N.; Hufnagel, G.; Galipaud, J.; Bertin, E.; Hassan, S. A.; Duca, M.; Roué, L.; Ruediger, A.; Garbarino, S.; Guay, D. J. Phys. Chem. C 2017, 121 (22), 12188–12198. (9) Garbarino, S.; Imbeault, R.; Sacré, N.; Guay, D. In Encyclopedia of Interfacial Chemistry: Surface Science and Electrochemistry ; 2017. (10) Imbeault, R.; Reyter, D.; Garbarino, S.; Roué, L.; Guay, D. J. Phys. Chem. C 2012, 116 (8), 5262–5269. Figure 1

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

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)0.000
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 score0.055
Threshold uncertainty score0.405

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.017
GPT teacher head0.222
Teacher spread0.205 · 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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Citations0
Published2020
Admission routes1
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