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Record W2142136513 · doi:10.1002/celc.201500295

Modification of TiO<sub>2</sub> Nanotubes with PtRu/Graphene Nanocomposites for Enhanced Oxygen Reduction Reaction

2015· article· en· W2142136513 on OpenAlexafffund
Walaa S. Alammari, Govindhan Maduraiveeran, Aicheng Chen

Bibliographic record

VenueChemElectroChem · 2015
Typearticle
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsLakehead University
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsGrapheneX-ray photoelectron spectroscopyNanocompositeMaterials scienceOxideElectrochemistryChemical engineeringTitanium dioxideScanning electron microscopeNanotechnologyElectrocatalystCathodeInorganic chemistryChemistryElectrodeComposite materialPhysical chemistryMetallurgy

Abstract

fetched live from OpenAlex

Abstract The oxygen reduction reaction (ORR) is one of the key fundamental reactions that occur at electrocatalytic cathode surfaces for fuel cell applications. Herein, we report the development of a series of nanocomposites of reduced graphene oxide (rGO) and PtRu nanoparticles with different atomic ratios of Pt/Ru deposited on titanium dioxide nanotubes (TiO 2 NTs) (termed as TiO 2 NT/rGO–PtRu) for the ORR. The TiO 2 NT/rGO–PtRu nanocomposites were prepared by using a facile one‐step electrochemical deposition, and characterized through field‐emission scanning electron microscopy, energy dispersive X‐ray spectroscopy, X‐ray diffraction, and X‐ray photoelectron spectroscopy. Our experimental results have shown that the synthesized TiO 2 NT/rGO–PtRu nanocomposite with a Pt/Ru ratio of 64:36 exhibits the highest electrocatalytic activity toward the ORR with an onset potential of 0.58 V (vs. RHE) and a high stability, which is promising for environmental and green energy applications.

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 score0.007
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.014
GPT teacher head0.226
Teacher spread0.212 · 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

Citations19
Published2015
Admission routes2
Has abstractyes

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