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Record W2036982219 · doi:10.1155/2012/389505

Highly Stable and Active Pt/Nb-TiO<sub>2</sub>Carbon-Free Electrocatalyst for Proton Exchange Membrane Fuel Cells

2012· article· en· W2036982219 on OpenAlexafffund
Shuhui Sun, Gaixia Zhang, Xueliang Sun, Mei Cai, Martin S. Ruthkosky

Bibliographic record

VenueJournal of Nanotechnology · 2012
Typearticle
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsInstitut National de la Recherche ScientifiqueWestern University
FundersGeneral Motors of CanadaNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsCanada Foundation for Innovation
KeywordsElectrocatalystMaterials scienceProton exchange membrane fuel cellMesoporous materialChemical engineeringCatalysisCathodeElectrochemistryPolystyreneCarbon fibersNanoparticleNanotechnologyFuel cellsElectrodeComposite materialOrganic chemistryComposite numberChemistryPolymerPhysical chemistry

Abstract

fetched live from OpenAlex

The current materials used in proton exchange membrane fuel cells (PEMFCs) are not sufficiently durable for commercial deployment. One of the major challenges lies in the development of an inexpensive, efficient, and highly durable and active electrocatalyst. Here a new type of carbon-free Pt/Nb-TiO 2 electrocatalyst has been reported. Mesoporous Nb-TiO 2 hollow spheres were synthesized by the sol-gel method using polystyrene (PS) sphere templates. Pt nanoparticles (NPs) were then deposited onto mesoporous Nb-TiO 2 hollow spheres via a simple wet-chemical route in aqueous solution, without the need for surfactants or potentiostats. The growth densities of Pt NPs on Nb-TiO 2 supports could be easily modulated by simply adjusting the experimental parameters. Electrochemical studies of Pt/Nb-TiO 2 show much enhanced activity and stability than commercial E-TEK Pt/C catalyst. PtNP/Nb-TiO 2 is a promising new cathode catalyst for PEMFC 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)0.001
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 score0.040
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
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 head0.007
GPT teacher head0.208
Teacher spread0.200 · 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

Citations32
Published2012
Admission routes2
Has abstractyes

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