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Record W2972297220 · doi:10.1088/1361-6528/ab4144

Surface-engineered cobalt nitride composite as efficient bifunctional oxygen electrocatalyst

2019· article· en· W2972297220 on OpenAlexaff
Yani Guan, Guihua Liu, Jingde Li, Yanji Wang, Zisheng Zhang

Bibliographic record

VenueNanotechnology · 2019
Typearticle
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsUniversity of Ottawa
FundersDepartment of Education of Hebei Province
KeywordsBifunctionalElectrocatalystOxygen evolutionMaterials scienceGrapheneNitrideCatalysisCobaltChemical engineeringOxideInorganic chemistryElectrolyteNanotechnologyElectrodeChemistryElectrochemistryMetallurgyPhysical chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract Efficient and low-cost bifunctional catalysts for oxygen reduction reaction (ORR) and oxygen evolution reaction (OER) are essential for the practical application of rechargeable metal–air batteries. In this work, we developed an efficient cobalt nitride hybrid bifunctional electrocatalyst, which consists of sulfur-doped and mildly oxidized Co 5.47 N nanoparticles supported on nitrogen-doped reduced graphene oxide sheet (O-S-Co 5.47 N@N-RGO). The composite exhibits good ORR-OER catalytic activity and excellent stability as well. It delivers an ORR half-wave potential of 0.82 V and an over-potential of 380 mV for OER at 10 mA cm −2 in 0.1 M KOH electrolyte. Density functional theory calculations indicate that the ORR activity of the composite might have originated from the Co-N 4 site in the RGO sheet, whereas the surface Co sites on O-S-Co 5.47 N crystal are responsible for its OER activity. The facile preparation method and insight into the ORR-OER active sites reported in this study advances the development of high-performance bifunctional oxygen electrocatalyst.

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 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 score0.082
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
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.007

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 head0.004
GPT teacher head0.199
Teacher spread0.194 · 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

Citations23
Published2019
Admission routes1
Has abstractyes

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