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Record W2613030675 · doi:10.1021/acssuschemeng.7b00655

Self-Assembly Synthesis of Cobalt- and Nitrogen-Coembedded Trumpet Flower-Like Porous Carbons for Catalytic Oxygen Reduction in Alkaline and Acidic Media

2017· article· en· W2613030675 on OpenAlexaff
Hao Jiang, Yisi Liu, Jiayu Hao, Yanqiu Wang, Wenzhang Li, Jie Li

Bibliographic record

VenueACS Sustainable Chemistry & Engineering · 2017
Typearticle
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsWestern University
FundersMinistry of Education of the People's Republic of ChinaNational Natural Science Foundation of China
KeywordsCatalysisCobaltMelamineNitrogenMethanolPyrolysisCarbon fibersChemistryOxygenInorganic chemistryOxygen reductionChemical engineeringPorosityMaterials scienceOrganic chemistryElectrochemistryElectrodeComposite material

Abstract

fetched live from OpenAlex

Developing highly cost-effective catalysts for an oxygen reduction reaction (ORR) in fuel cells is highly significant but still full of challenges. In this work, cobalt- and nitrogen-coembedded three-dimensional (3D) trumpet flower-like porous carbons (CoNC) have been prepared by a simple two-step self-assembly technique, using carbon quantum dots (CQDs) as the carbon precursor and a supermolecular gel of self-assembled melamine and Co 2+ ions as the nitrogen and cobalt sources. The resultant CoNC catalysts possess unique 3D trumpet flower-like structures, efficient charge transfer ability, and abundant Co–N x active sites. As a catalyst for ORR, the optimized CoNC-800 (pyrolyzed at 800 °C) exhibits efficient electrocatalytic activities, longer-term stability, and strong endurance to methanol both in acidic and alkaline media. It can be worked as a prospective substitute for a commercial Pt/C catalyst for ORR in the widespread implementation of fuel cells.

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)0.001
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 score0.003
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.004
GPT teacher head0.196
Teacher spread0.192 · 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

Citations62
Published2017
Admission routes1
Has abstractyes

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