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Record W3023846881 · doi:10.1021/acsaem.0c00069

OER Performances of Cationic Substituted (100)-Oriented IrO<sub>2</sub> Thin Films: A Joint Experimental and Theoretical Study

2020· article· en· W3023846881 on OpenAlexafffund
Gaëtan Buvat, Mohammad J. Eslamibidgoli, Sébastien Garbarino, Michael Eikerling, Daniel Guay

Bibliographic record

VenueACS Applied Energy Materials · 2020
Typearticle
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsSimon Fraser UniversityCollège de MaisonneuveInstitut National de la Recherche Scientifique
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCationic polymerizationOxygen evolutionDensity functional theoryThin filmCatalysisMaterials scienceDeposition (geology)Pulsed laser depositionCharacterization (materials science)ElectrocatalystChemical engineeringPhysical chemistryChemistryNanotechnologyComputational chemistryElectrodeElectrochemistryPolymer chemistryOrganic chemistryGeology

Abstract

fetched live from OpenAlex

Cationic substitution was investigated as a strategy to increase the electrocatalytic activity of IrO2-based films for the oxygen evolution reaction (OER). For this purpose, an approach that combines detailed experimental characterization with quantum mechanical calculations based on density functional theory was employed. A series of (100)-oriented Ir1–xMxO2 thin films, with M = Ni, Cr, Mo, W, Sn, Pt, Rh, Ru, V, and Mn, was prepared with a one-step synthesis approach based on pulsed laser deposition, and the electrocatalytic activity of these films for the OER was measured. Matching material compositions and structures were generated in silico for DFT-based calculations of their electronic structure and OER pathway. A comparison of the experimental and theoretical results revealed the viable activity descriptor, paving the way for a systematic search to find the most active Ir-based OER catalyst.

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 categoriesMeta-epidemiology (narrow)
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 score0.255
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.212
Teacher spread0.203 · 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.

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

Citations18
Published2020
Admission routes2
Has abstractyes

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