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Record W2294279908 · doi:10.1021/acsami.5b11870

Highly Functional Bioinspired Fe/N/C Oxygen Reduction Reaction Catalysts: Structure-Regulating Oxygen Sorption

2016· article· en· W2294279908 on OpenAlexaff
Yingfang Yao, Yong You, Gaixia Zhang, Jianguo Liu, Haoran Sun, Zhigang Zou, Shuhui Sun

Bibliographic record

VenueACS Applied Materials & Interfaces · 2016
Typearticle
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsInstitut National de la Recherche Scientifique
FundersPriority Academic Program Development of Jiangsu Higher Education InstitutionsNatural Science Foundation of Jiangsu ProvinceGovernment of Jiangsu ProvinceMinistry of Science and Technology of the People's Republic of ChinaMinistry of Education of the People's Republic of ChinaNational Natural Science Foundation of China
KeywordsOxygen reduction reactionCatalysisMaterials scienceSorptionOxygenOxygen reductionChemical engineeringOrganic chemistryAdsorptionElectrochemistryChemistryPhysical chemistryElectrode

Abstract

fetched live from OpenAlex

Tuna is one of the most rapid and distant swimmers. Its unique gill structure with the porous lamellae promotes fast oxygen exchange that guarantees tuna's high metabolic and athletic demands. Inspired by this specific structure, we designed and fabricated microporous graphene nanoplatelets (GNPs)-based Fe/N/C electrocatalysts for oxygen reduction reaction (ORR). Careful control of GNP structure leads to the increment of microporosity, which influences the O2 adsorption positively and desorption oppositely, resulting in enhanced O2 diffusion, while experiencing reduced ORR kinetics. Working in the cathode of proton-exchange membrane fuel cells, the GNP catalysts require a compromise between adsorption/desorption for effective O2 exchange, and as a result, appropriate microporosity is needed. In this work, the highest power density, 521 mW·cm(-2), at zero back pressure is achieved.

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 score0.012
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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.011
GPT teacher head0.209
Teacher spread0.198 · 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

Citations45
Published2016
Admission routes1
Has abstractyes

Explore more

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