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Record W2883506114 · doi:10.1149/2.0021815jes

Non Noble Metal Catalyst for Oxygen Reduction Reaction and Its Characterization by Simulated Fuel Cell Test

2018· article· en· W2883506114 on OpenAlexaff
Yunfeng Zhan, Fangyan Xie, Hao Zhang, Zhipeng Lin, Jilin Huang, Weihong Zhang, Xueliang Sun, Yueli Zhang, Jian Chen, Hui Meng

Bibliographic record

VenueJournal of The Electrochemical Society · 2018
Typearticle
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsWestern University
FundersScience and Technology Planning Project of Guangdong ProvinceNatural Science Foundation of Guangdong ProvinceNational Natural Science Foundation of China
KeywordsCatalysisGrapheneCarbon fibersPyrolysisMaterials scienceNoble metalChemical engineeringNitrogenInorganic chemistryOxygen reduction reactionFuel cellsDopingAlkaline fuel cellChemistryElectrochemistryNanotechnologyComposite materialComposite numberOrganic chemistryElectrodeElectrolytePhysical chemistry

Abstract

fetched live from OpenAlex

In this work nitrogen doped multi-layer graphene and porous carbon are prepared by the pyrolysis of glycine as precursor. The morphology of the product is controlled by the catalyst used. The nitrogen doped multi-layer graphene exhibits a high onset potentialand four-electron pathway for oxygen reduction reaction (ORR) in alkaline solution as well as much enhanced stability compared with commercial Pt/C catalyst. In a simulated fuel cell test the nitrogen doped multi-layer graphene shows comparable performance with Pt/C catalyst in alkaline environment. The ORR activity of the as-prepared carbon material is found to be related with graphitization degree and graphitic-N.

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 score0.002
Threshold uncertainty score0.005

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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.005
GPT teacher head0.209
Teacher spread0.204 · 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

Citations13
Published2018
Admission routes1
Has abstractyes

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Same venueJournal of The Electrochemical SocietySame topicElectrocatalysts for Energy ConversionFrench-language works237,207