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Record W3003226388 · doi:10.1021/jacs.9b13313

Preserving the Exposed Facets of Pt<sub>3</sub>Sn Intermetallic Nanocubes During an Order to Disorder Transition Allows the Elucidation of the Effect of the Degree of Alloy Ordering on Electrocatalysis

2020· article· en· W3003226388 on OpenAlexaff
Hsiang-Sheng Chen, Tânia M. Benedetti, Vinícius R. Gonçales, Nicholas M. Bedford, Robert W. J. Scott, Richard F. Webster, Soshan Cheong, J. Justin Gooding, Richard D. Tilley

Bibliographic record

VenueJournal of the American Chemical Society · 2020
Typearticle
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsUniversity of Saskatchewan
FundersArgonne National LaboratoryDivision of Materials ResearchOffice of ScienceAustralian Research CouncilUniversity of New South WalesAnalytical Center for the Government of the Russian FederationU.S. Department of Energy
KeywordsElectrocatalystChemistryAlloyIntermetallicTinNanocrystalDissolutionChemical engineeringNanoparticlePlatinumNanotechnologyChemical physicsCatalysisCrystallographyElectrochemistryElectrodeMaterials sciencePhysical chemistry

Abstract

fetched live from OpenAlex

Controlling which facets are exposed in nanocrystals is crucial to understanding different activity between ordered and disordered alloy electrocatalysts. We modify the degree of ordering of Pt3Sn nanocubes, while maintaining the shape and size, to enable a direct evaluation of the effect of the order on ORR catalytic activity. We demonstrate a 2.3-fold enhancement in specific activity by 60- and 30%-ordered Pt3Sn nanocubes compared to 95%-ordered. This was shown to be likely due to surface vacancies in the less-ordered particles. The greater order, however, results in higher stability of the electrocatalyst, with the more disordered nanoparticles showing the dissolution of tin and platinum species during electrocatalysis.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.015
Threshold uncertainty score0.468

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0000.001
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 head0.009
GPT teacher head0.213
Teacher spread0.204 · 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".

Quick stats

Citations86
Published2020
Admission routes1
Has abstractyes

Explore more

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