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Record W2017496873 · doi:10.1149/1.2975191

High-Performance Osmium Nanoparticle Electrocatalyst for Direct Borohydride PEM Fuel Cell Anodes

2008· article· en· W2017496873 on OpenAlexafffund
V. W. S. Lam, Előd Gyenge

Bibliographic record

VenueJournal of The Electrochemical Society · 2008
Typearticle
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of British ColumbiaGoddard Space Flight CenterUniversity of Windsor
KeywordsOsmiumBorohydrideElectrocatalystAnodeProton exchange membrane fuel cellElectrochemistryContext (archaeology)ChemistryCatalysisChemical engineeringNanoparticleKineticsCathodeElectrochemical energy conversionMaterials scienceInorganic chemistryElectrodeNanotechnologyRutheniumPhysical chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Carbonaceous direct fuel cells are hampered by sluggish anode kinetics associated with CO poisoning and therefore typically require a high load of costly Pt-based electrocatalysts. Direct borohydride fuel cells (DBFCs) have an inherent advantage due to the absence of CO and are characterized by high thermodynamic specific energy . Here we show for the first time, using fundamental electrochemical methods combined with fuel cell experiments, that osmium nanoparticles are kinetically superior and stable catalysts for borohydride electro-oxidation compared to Pt and PtRu. Osmium favors the direct oxidation of by a total of seven electrons as opposed to in situ hydrogen generation. The complex network of reactions involved in the oxidation of to is analyzed in the context of the experimental data. The current densities obtained with DBFC at with 20% anode were at and at .

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.195
Teacher spread0.189 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations80
Published2008
Admission routes2
Has abstractyes

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