MétaCan
Menu
Back to cohort
Record W2543669366 · doi:10.1002/chem.201604162

Synthesis of Cobalt Sulfide/Sulfur Doped Carbon Nanocomposites with Efficient Catalytic Activity in the Oxygen Evolution Reaction

2016· article· en· W2543669366 on OpenAlexaff
Huayu Qian, Jing Tang, Zhongli Wang, Jeonghun Kim, Jung Ho Kim, Saad M. Alshehri, E. Yanmaz, Xin Wang, Yusuke Yamauchi

Bibliographic record

VenueChemistry - A European Journal · 2016
Typearticle
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsMinistry of Education and Child Care
FundersNanjing University of Science and TechnologyNational Science Foundation
KeywordsSulfurCobalt sulfideCobaltCatalysisSulfideNanocompositeCarbon fibersOxygenDopingChemistryInorganic chemistryMaterials scienceChemical engineeringNanotechnologyOrganic chemistryPhysical chemistryComposite numberElectrodeElectrochemistry

Abstract

fetched live from OpenAlex

Abstract Cobalt sulfide/sulfur doped carbon composites (Co 9 S 8 /S‐C) were synthesized by calcining a rationally designed sulfur‐containing cobalt coordination complex in an inert atmosphere. From the detailed transmission electron microscopy (TEM) and X‐ray photoelectron spectroscopy (XPS) analyses, the electrocatalytically active Co 9 S 8 nanoparticles were clearly obtained and combined with the thin sulfur doped carbon layers. Electrochemical data showed that Co 9 S 8 /S‐C had a good activity and long‐term stability in catalyzing oxygen evolution reaction in alkaline electrolyte, even better than the traditional RuO 2 electrocatalyst. The excellent electrocatalytic activity of Co 9 S 8 /S‐C was mainly attributed to the synergistic effect between the Co 9 S 8 catalyst which contributed to the oxygen evolution reaction and the sulfur doped carbon layer which facilitated the adsorption of reactants, prevented the Co 9 S 8 particles from aggregating and served as the electrically conductive binder between each component.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.013
Threshold uncertainty score0.498

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.006
GPT teacher head0.188
Teacher spread0.181 · 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.

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

Citations56
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueChemistry - A European JournalSame topicElectrocatalysts for Energy ConversionFrench-language works237,207