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Record W2970662147 · doi:10.1002/batt.201900102

A Gas Diffusion Layer Impregnated with Mn<sub>3</sub>O<sub>4</sub>‐Decorated N‐Doped Carbon Nanotubes for the Oxygen Reduction Reaction in Zinc‐Air Batteries

2019· article· en· W2970662147 on OpenAlexafffund
Drew Aasen, M.P. Clark, Douglas G. Ivey

Bibliographic record

VenueBatteries & Supercaps · 2019
Typearticle
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCatalysisBifunctionalMaterials scienceElectrochemistryCarbon nanotubeGas diffusion electrodeChemical engineeringElectrodeCarbon fibersBattery (electricity)Bifunctional catalystComposite numberDiffusionInorganic chemistryChemistryNanotechnologyComposite material

Abstract

fetched live from OpenAlex

Abstract Mn 3 O 4 ‐decorated N‐CNTs are synthesized and impregnated into porous carbon paper (gas diffusion layer or GDL) to form a composite catalyst‐GDL material in a simple and novel one‐pot process. The impregnated electrode features high active surface area, improved discharge performance, and reduced vulnerability to flooding when compared with other electrode preparation techniques for similar catalysts. Electrochemical and battery testing show catalytic activity and a maximum discharge potential superior to other CNT supported Mn 3 O 4 catalysts, and comparable to commercially used Pt−Ru (1.21 V at 20 mA cm −2 ). The composite is cycled at 10 mA cm −2 and 20 mA cm −2 as a bifunctional catalyst and as an oxygen reduction reaction (ORR) exclusive catalyst, respectively. Discharge performance is stable over 200 cycles at 20 mA cm −2 when used exclusively for ORR with a discharge‐charge efficiency superior to Pt−Ru when coupled with electrodeposited Co−Fe as the OER catalyst (efficiency of 59 % after cycling).

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)
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
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.008
GPT teacher head0.197
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 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

Citations22
Published2019
Admission routes2
Has abstractyes

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