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Record W2315531681 · doi:10.1021/am300002j

Mesoporous Nanostructured Nb-Doped Titanium Dioxide Microsphere Catalyst Supports for PEM Fuel Cell Electrodes

2012· article· en· W2315531681 on OpenAlexaff
Laure Chevallier, Alexander Bauer, Sara Cavalière, Rob Hui, Jacques Rozière, Deborah J. Jones

Bibliographic record

VenueACS Applied Materials & Interfaces · 2012
Typearticle
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsBC Innovation Council
FundersCentre National de la Recherche Scientifique
KeywordsMaterials scienceMesoporous materialProton exchange membrane fuel cellElectrocatalystChemical engineeringCyclic voltammetryElectrolyteCathodeElectrochemistryTitanium dioxideCatalysisElectrodeInorganic chemistryComposite materialFuel cellsOrganic chemistry

Abstract

fetched live from OpenAlex

Crystalline microspheres of Nb-doped TiO(2) with a high specific surface area were synthesized using a templating method exploiting ionic interactions between nascent inorganic components and an ionomer template. The microspheres exhibit a porosity gradient, with a meso-macroporous kernel, and a mesoporous shell. The material has been investigated as cathode electrocatalyst support for polymer electrolyte membrane (PEM) fuel cells. A uniform dispersion of Pt particles on the Nb-doped TiO(2) support was obtained using a microwave method, and the electrochemical properties assessed by cyclic voltammetry. Nb-TiO(2) supported Pt demonstrated very high stability, as after 1000 voltammetric cycles, 85% of the electroactive Pt area remained compared to 47% in the case of commercial Pt on carbon. For the oxygen reduction reaction (ORR), which takes place at the cathode, the highest stability was again obtained with the Nb-doped titania-based material even though the mass activity calculated at 0.9 V vs RHE was slightly lower. The microspherical structured and mesoporous Nb-doped TiO(2) is an alternative support to carbon for PEM fuel cells.

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 score0.007
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
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.006
GPT teacher head0.214
Teacher spread0.207 · 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

Citations81
Published2012
Admission routes1
Has abstractyes

Explore more

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