MétaCan
Menu
Back to cohort
Record W2330377686 · doi:10.1021/jp3023875

Targeting Chemical Morphology of Graphene Oxide for Self-Assembly and Subsequent Templating of Nanoparticles: A Composite Approaching Capacitance Limits in Graphene

2012· article· en· W2330377686 on OpenAlexaff
Shehan Salgado, Long Pu, Vivek Maheshwari

Bibliographic record

VenueThe Journal of Physical Chemistry C · 2012
Typearticle
Languageen
FieldMaterials Science
TopicGraphene research and applications
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsGrapheneMaterials scienceNanoparticleOxideNanotechnologyNanomaterialsComposite numberCapacitanceMorphology (biology)Chemical engineeringComposite materialElectrodeChemistryMetallurgy

Abstract

fetched live from OpenAlex

The chemical morphology of graphene oxide sheets (GO) is characterized by the presence of functional carboxylic groups (at the edges) and epoxy and hydroxyl groups (at basal plane). Using divalent calcium ions, this morphology is used to bind the sheets together into an exfoliated sheet assembly or compact multilayers by controlling the pH. The assembly provides a 3-D template where the adjacent GO sheets mechanically constraint the synthesis of nanomaterials. Using this template, nanoparticles are directly synthesized on the sheets with high density and uniform shape and size, without a capping agent. Both noble metal Au and Ag nanoparticles and inorganic CaCO 3 nanoparticles are synthesized. The formation of nanoparticles leads to local straining of the sheets, and the location of the nanoparticles coincides with the formation of radial wrinkles on the sheets. Further electrochemical capacitors made with GO–CaCO 3 composite shows capacitance values of 240 F/g, approaching the theoretical limits of 550 F/g, as the CaCO 3 nanoparticles act as spacers between the sheets preserving their surface area.

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 score0.004
Threshold uncertainty score0.313

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 head0.022
GPT teacher head0.276
Teacher spread0.254 · 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

Citations32
Published2012
Admission routes1
Has abstractyes

Explore more

Same venueThe Journal of Physical Chemistry CSame topicGraphene research and applicationsFrench-language works237,207