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Record W4387575000 · doi:10.1002/cctc.202300668

Strong Metal‐Support Interactions in ZrO<sub>2</sub>‐Supported IrO<sub>x</sub> Catalyst for Efficient Oxygen Evolution Reaction

2023· article· en· W4387575000 on OpenAlexafffund
Himanshi Dhawan, Xuehai Tan, Jing Shen, James Woodford, Marc Secanell, Natalia Semagina

Bibliographic record

VenueChemCatChem · 2023
Typearticle
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsUniversity of Alberta
FundersCanada First Research Excellence Fund
KeywordsCatalysisElectrocatalystOxygen evolutionDesorptionDissolutionElectrochemistryOxygenInorganic chemistryMetalChemistryMaterials scienceAdsorptionChemical engineeringPhysical chemistryElectrodeMetallurgy

Abstract

fetched live from OpenAlex

Abstract The use of ZrO 2 as a support material for IrO x ‐based catalysts in oxygen evolution reaction (OER) electrocatalysis was studied using ex‐situ characterization and rotating disk electrode electrochemical testing of supported Ir x Zr (1‐x) O 2 on ZrO 2 of varying sizes. The catalyst exhibited high OER mass (specific) activity (712 A ) and intrinsic activity (4.8 mA ) at 1.6 V RHE, attributed to Ir x Zr (1‐x) O 2 alloy formation, an interconnected network of Ir x Zr (1‐x) O 2 nanoparticles and the presence of Ir(III)/Ir(IV) species throughout the bulk. It also appears to be resistant to Ir dissolution; however, accumulation of O 2 bubbles in the catalyst microstructure and minor phase transformation of Ir(III)/Ir(IV) species during OER cause deactivation. Temperature‐programmed desorption indicated a possible link between the observed high activity and higher amounts of adsorbed H 2 O and desorbed O 2 species.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.006
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.017
GPT teacher head0.250
Teacher spread0.233 · 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

Citations4
Published2023
Admission routes2
Has abstractyes

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