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Record W2111902542 · doi:10.1002/chem.201500836

Chemical Modification of Graphene Oxide through Diazonium Chemistry and Its Influence on the Structure–Property Relationships of Graphene Oxide–Iron Oxide Nanocomposites

2015· article· en· W2111902542 on OpenAlexaff
Valentina Rebuttini, Enza Fazio, S. Santangelo, F. Neri, Gianvito Caputo, Cédric Martin, Thierry Brousse, Frèdéric Favier, Nicola Pinna

Bibliographic record

VenueChemistry - A European Journal · 2015
Typearticle
Languageen
FieldMaterials Science
TopicSupercapacitor Materials and Fabrication
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsGrapheneOxideNanocompositeElectrochemistryMaterials scienceNanoparticleIron oxideCovalent bondAdsorptionChemical engineeringSupercapacitorSurface modificationChemistryNanotechnologyOrganic chemistryElectrodePhysical chemistry

Abstract

fetched live from OpenAlex

4-Carboxyphenyl groups are covalently grafted onto graphene oxide via diazonium chemistry for studying their role on the adsorption of iron oxide nanoparticles. The nanoparticles are deposited via a novel phase-transfer approach involving specific interactions at the interface between two immiscible solvents. The increased density and the homogeneous distribution of surface carboxyl moieties enable the preparation of a nanocomposite with improved iron oxide distribution and loading. Structure-properties relationships are investigated by analysing the electrochemical properties of the nanocomposites, which are regarded as promising active materials for application in supercapacitors. It is demonstrated that the nature of the interactions between the components similarly affects the overall electrochemical performances of the nanocomposites and the structure of the materials.

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)0.001
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 score0.006
Threshold uncertainty score0.716

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 science0.0010.000
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 head0.041
GPT teacher head0.233
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.

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

Citations44
Published2015
Admission routes1
Has abstractyes

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