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Record W2561465008 · doi:10.3390/catal7010001

The New Graphene Family Materials: Synthesis and Applications in Oxygen Reduction Reaction

2016· article· en· W2561465008 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueCatalysts · 2016
Typearticle
Languageen
FieldMaterials Science
TopicConducting polymers and applications
Canadian institutionsInstitut National de la Recherche Scientifique
FundersFonds de recherche du Québec – Nature et technologiesNatural Sciences and Engineering Research Council of CanadaGuizhou Science and Technology DepartmentCentre québécois sur les matériaux fonctionnelsChina Scholarship CouncilGuizhou Normal UniversityInstitut national de la recherche scientifique
KeywordsGrapheneMaterials scienceNanotechnologyOxygen reduction reactionFuel cellsOxygen reductionChemistryChemical engineeringElectrochemistryElectrodeEngineering

Abstract

fetched live from OpenAlex

Graphene family materials, including graphene quantum dots (GQDs), graphene nanoribbons (GNRs) and 3D graphene (3D-G), have attracted much research interest for the oxygen reduction reaction (ORR) in fuel cells and metal-air batteries, due to their unique structural characteristics, such as abundant activate sites, edge effects and the interconnected network. In this review, we summarize recent developments in fabricating various new graphene family materials and their applications for use as ORR electrocatalysts. These new graphene family materials play an important role in improving the ORR performance, thus promoting the practical use in metal-air batteries and 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.

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 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 score0.027
Threshold uncertainty score0.189

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 communication0.0000.000
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 head0.018
GPT teacher head0.249
Teacher spread0.231 · 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