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Record W2114032312 · doi:10.1002/cssc.201300127

Platinum‐ and Membrane‐Free Swiss‐Roll Mixed‐Reactant Alkaline Fuel Cell

2013· article· en· W2114032312 on OpenAlexaff
Amin Aziznia, Colin Oloman, Előd Gyenge

Bibliographic record

VenueChemSusChem · 2013
Typearticle
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsBorohydrideAnodeOsmiumCathodeProton exchange membrane fuel cellChemistryCatalysisPlatinumInorganic chemistryAlkaline fuel cellPower densityDirect-ethanol fuel cellChemical engineeringMaterials scienceElectrolyteElectrodeRutheniumOrganic chemistryPhysical chemistry

Abstract

fetched live from OpenAlex

Eliminating the expensive and failure-prone proton exchange membrane (PEM) together with the platinum-based anode and cathode catalysts would significantly reduce the high capital and operating costs of low-temperature (<373 K) fuel cells. We recently introduced the Swiss-roll mixed-reactant fuel cell (SR-MRFC) concept for borohydride-oxygen alkaline fuel cells. We now present advances in anode electrocatalysis for borohydride electrooxidation through the development of osmium nanoparticulate catalysts supported on porous monolithic carbon fiber materials (referred to as an osmium 3D anode). The borohydride-oxygen SR-MRFC operates at 323 K and near atmospheric pressure, generating a peak power density of 1880 W m(-2) in a single-cell configuration by using an osmium-based anode (with an osmium loading of 0.32 mg cm(-2)) and a manganese dioxide gas-diffusion cathode. To the best of our knowledge, 1880 W m(-2) is the highest power density ever reported for a mixed-reactant fuel cell operating under similar conditions. Furthermore, the performance matches the highest reported power densities for conventional dual chamber PEM direct borohydride 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
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 score0.190
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
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.185
Teacher spread0.178 · 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; both teacher heads agree on what is shown here.

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

Citations18
Published2013
Admission routes1
Has abstractyes

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