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Record W3186698096 · doi:10.1149/ma2021-01451823mtgabs

Structure-Dependence of the Atomic-Scale Mechanisms of Pt Electrooxidation and Dissolution

2021· article· en· W3186698096 on OpenAlexaff
Jakub Drnec, Timo Fuchs, Federico Calle‐Vallejo, Natalie Stubb, Daniel J. S. Sandbeck, Martin Ruge, Serhiy Cherevko, David A. Harrington, Olaf M. Magnussen

Bibliographic record

VenueECS Meeting Abstracts · 2021
Typearticle
Languageen
FieldMaterials Science
TopicElectron and X-Ray Spectroscopy Techniques
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsDissolutionPlatinumPerchloric acidOxideDensity functional theoryCatalysisElectrochemistryChemical physicsChemistryAtomic unitsCathodeMaterials scienceInorganic chemistryChemical engineeringElectrodePhysical chemistryComputational chemistryMetallurgy

Abstract

fetched live from OpenAlex

For current low-temperature hydrogen-oxygen fuel cells, as used e.g. in the upcoming generation of commercial fuel cell vehicles, platinum is still the catalyst of choice, especially for the cathode side. However, catalyst lifetime and performance remain a problem for large-scale commercialization. It has been known for some time that electrochemical oxidation and subsequent oxide reduction lead to irreversible structural changes as well platinum dissolution but the fundamental understanding of these complex processes is still rudimentary. A detailed atomistic picture of the steps by which these ultrathin oxide layers form existed up to now only for the close-packed Pt(111) surface [1]. Here, previous in situ studies by surface X-ray diffraction (SXRD) techniques established the precise locations of the first Pt atoms that move out of their lattice sites in a place-exchange process [2] and found good agreement with density functional theory (DFT) calculations [3]. Provided the potential is not too high, these atoms can move back to their original locations, but at higher potentials an irreversible restructuring of the surface occurs. To elucidate the role of the Pt surface structure, we performed in-depth comparative studies of Pt(111) and Pt(100) in perchloric acid solution by in situ SXRD, online inductively coupled plasma mass spectrometry (ICP-MS) and DFT [4]. Clear differences were found, which mirror the differences in the dissolution behavior of the two electrode surfaces ( Figure 1a ). Pt(100) not only start to dissolve at lower potential but its oxidation also immediately changes its surface structure in an irreversible manner. These observations demonstrate clearly that Pt dissolution is linked to the way Pt atoms are initially extracted from the surfaces during oxidation. For a better understanding of the extraction process on Pt(100), we performed surface diffraction studies at ESRF ID31 with very high photon energies of 70 keV (HESXRD) [5]. This technique was employed for the first time to a solid-liquid interface and proofed to be indispensable for clarifying the oxide’s structure. The oxidation-induced changes, exemplified in figure 1b for one out of twelve simultaneously measured crystal truncation rods, can be unambiguously assigned to a process in which the extracted Pt atom moves laterally away from its original site. This initiates the immediate extraction of a second atom, leading to the formation of atomic oxide stripes on the surface (see figure 1c ). DFT calculations support this scenario and show that, differently from Pt(111), this mechanism produces unstable surface atoms at stripe ends that can dissolve during the oxidation as well as during the subsequent oxide reduction. Consequently, the extraction process on Pt(100) is irreversible from its very onset. These results show a high sensitivity of Pt electrocatalyst oxidation and degradation to the precise surface structure. Rational strategies for the design of catalysts with improved stability will have to take the different mechanisms into account. [1] J. Drnec, D.A. Harrington, O.M. Magnussen, Curr. Op. Electrochem. , 4 , 69-75 (2017) [2] M. Ruge, et al., J. Electrochem. Soc. , 164 , H608-H614 (2017) [3] M. J. Eslamibidgoli,, M. H. Eikerling, Electrocatalysis , 7 , 345–354 (2016) [4] T. Fuchs, et al., Nat. Catal. , 3 , 754-761 (2020) [5] J. Gustafson, et al., Science , 343 , 758–761 (2014) Figure 1. (a) correlation between Pt(hkl) dissolution and restructuring behavior in 0.1 M HClO 4 . (b) Example of Pt(100) crystal truncation rod changes with increasing oxidation, measured by in situ high-energy surface X-ray diffraction. (c) Pt extraction mechanisms, obtained from DFT calculations (adapted from T. Fuchs, et al., Nat. Catal. , 3 , 754 (2020)). 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)0.000
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 score0.026
Threshold uncertainty score0.314

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 head0.007
GPT teacher head0.244
Teacher spread0.237 · 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
Published2021
Admission routes1
Has abstractyes

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