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Record W2021670843 · doi:10.1002/cctc.201403026

Graphene‐Supported Substoichiometric Sodium Tantalate as a Methanol‐Tolerant, Non‐Noble‐Metal Catalyst for the Electroreduction of Oxygen

2015· article· en· W2021670843 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueChemCatChem · 2015
Typearticle
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsMethanolCatalysisElectrocatalystGrapheneMaterials scienceInorganic chemistryOxideTantalateTantalumNoble metalThermal stabilityChemical engineeringChemistryElectrochemistryNanotechnologyOrganic chemistryMetallurgy

Abstract

fetched live from OpenAlex

Abstract For the oxygen reduction reaction (ORR) in acidic medium, a good combination of an active and stable catalyst highly dispersed on a conductive and durable support is required. Moreover, for direct methanol fuel‐cell applications, a suitable tolerance to methanol poisoning is also necessary. In this communication, we report on a new graphene‐supported sodium tantalate (Na 2 Ta 8 O 21− x ) electrocatalyst obtained at high temperature and characterized by significant substoichiometry. The synthesis includes the precipitation of tantalum oxide on a high surface area graphene and subsequent thermal treatment at 900 °C. A novel sodium tantalate phase with oxygen vacancies is thereafter obtained. This catalyst formulation shows promising activity towards the ORR especially in the presence of a high methanol concentration, as evidenced by a high tolerance to methanol poisoning. Accelerated stress tests and chemical leaching experiments also show the remarkable stability of the catalyst. This material presents good perspectives for application in cost‐effective 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.

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 score0.012
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
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.020
GPT teacher head0.248
Teacher spread0.228 · 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