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Record W4285398240 · doi:10.1149/ma2022-01451944mtgabs

New Insights into Pt Dissolution Mechanisms from SFC-ICP-MS Measurements for Well-Defined Surfaces

2022· article· en· W4285398240 on OpenAlexaff
Valentín Briega‐Martos, Timo Fuchs, Jakub Drnec, David A. Harrington, Olaf M. Magnussen, Serhiy Cherevko

Bibliographic record

VenueECS Meeting Abstracts · 2022
Typearticle
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsDissolutionInductively coupled plasma mass spectrometryProton exchange membrane fuel cellCathodeElectrolyteChemical engineeringCatalysisNanoparticleMaterials sciencePlatinumInductively coupled plasmaAnodeChemistryNanotechnologyMass spectrometryAnalytical Chemistry (journal)ElectrodeChromatography

Abstract

fetched live from OpenAlex

In the last years, polymer electrolyte membrane fuel cell (PEMFC) technology has progressed notably, allowing its extended implementation in the transport sector. For example, the fuel cell cars currently being commercialized use PEMFCs with Pt nanoparticles for the oxygen reduction reaction (ORR) at the cathode, primarily because of their better long-term stability in comparison to other electrocatalysts. However, even pure Pt catalysts degrade under real-life conditions, since a PEMFC is expected to resist hundreds of thousands of load cycles and tens of thousands of start-up/shut-down cycles during its lifetime, leading to a high number of platinum oxidation/reduction cycles and resulting in its extensive degradation.1 This degradation of Pt catalysts is mainly linked to electro-oxidation and dissolution processes, which have been investigated for a long time in polycrystalline and supported nanoparticle catalysts. More recently, investigations with Pt single crystal electrodes have been carried out, which offer the possibility of a more detailed understanding of these processes at the atomic level. These works include studies using in-situ surface X-ray diffraction (SXRD) and on-line inductively coupled mass spectrometry (ICP-MS), which have for example shown differences in the onset potential for anodic dissolution on Pt(100) and Pt(111) that have their origin in the different atomic structures of the initial oxide.2 The present work focuses on the dissolution behaviour of the well-defined surfaces of the three Pt basal planes investigated by scanning flow cell inductively coupled mass spectrometry (SFC-ICP-MS). Further investigations providing new information about the dissolution mechanisms are carried out by varying a wide variety of parameters: upper potential limit, scan rate, potential holding times, electrolyte composition, pH, purging gas and cooling atmosphere. Some of the results will be presented in combination with SXRD measurements in order to explain different aspects of the restructuring and dissolution mechanisms for these surfaces. For example, the dissolved amounts for Pt(111), Pt(100) and Pt(110) in 0.1 M HClO4 and 0.1 M H2SO4 after cyclic voltammetry at a scan rate of 0.05 V s-1 for three different upper potential limits, 1.20 V, 1.40 V and 1.60 V vs. RHE, were obtained. In all cases dissolution follows the order Pt(110) > Pt(100) > Pt(111), which is in agreement with the previous works performed in 0.1 M HClO4.3 Higher dissolution is always observed in the case of H2SO4 electrolyte. Potentiostatic hold experiments for Pt(100) have been carried out, allowing the separation of the anodic and cathodic dissolution peaks. It can be observed that the cathodic dissolution increases proportionally with the increase in the oxidation potential. The combination of this data with SXRD measurements suggests that there are specific species in the oxide structure at high potentials that are the responsible for the majority of the cathodic dissolution in the Pt(100) surface. References M. K. Debe, Nature, 486 (2012) 43-51. Fuchs, T.; Drnec, J.; Calle-Vallejo, F.; Stubb, N.; Sandbeck, D.; Ruge, M.; Cherevko. S.; Harrington, D. A.; Magnussen, O. M. Nature Catalysis 3 (2020) 754-761. Sandbeck, D. J. S.; Brummel, O.; Mayrhofer, K. J. J.; Stubb, N.; Libuda, J.; Katsounaros, I.; Cherevko. S. ChemPhysChem 20 (2019) 2997-3003

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 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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.001

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 head0.019
GPT teacher head0.227
Teacher spread0.208 · 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 designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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Same venueECS Meeting Abstracts→Same topicElectrocatalysts for Energy Conversion→French-language works237,207→