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Record W2089145213 · doi:10.1021/jp045147h

Functionalization of Multiwalled Carbon Nanotubes by Mild Aqueous Sonication

2005· article· en· W2089145213 on OpenAlexaff
De‐Quan Yang, Jean-Francois Rochette, E. Sacher

Bibliographic record

VenueThe Journal of Physical Chemistry B · 2005
Typearticle
Languageen
FieldMaterials Science
TopicCarbon Nanotubes in Composites
Canadian institutionsPolytechnique MontréalRegroupement Québécois sur les Matériaux de Pointe
Fundersnot available
KeywordsSonicationSurface modificationAqueous solutionCarbon nanotubeRaman spectroscopyMaterials scienceChemical engineeringFourier transform infrared spectroscopySubstrate (aquarium)NanotechnologyOrganic chemistryChemistry

Abstract

fetched live from OpenAlex

Sonication has been widely used in the dispersal of carbon nanotubes (CNTs) in various liquids as well as in their functionalization in aqueous acids. Here, for the first time, we study the sonication of multiwalled CNTs (MWCNTs) in deionized water. Our results indicate an improvement in the aqueous dispersal of MWCNTs as well as an increase in their adhesive interaction with Au substrates. Field emission scanning electron and high-resolution transmission electron microscopies as well as X-ray photoelectron, photoacoustic Fourier transform IR, and Raman spectroscopies have shown this to be due to the production of low concentrations of O-containing functionalizations (alcohol, carbonyl, acid, with the total O concentration being approximately 2%), without damaging the basic CNT structure; this production of functional groups is mirrored by the disappearance of -CH(n) groups existing on the pristine CNTs. These new functional groups are capable of hydrogen bonding, which plays an important role in their aqueous dispersal and enhanced substrate interactions.

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)0.000
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 score0.005
Threshold uncertainty score0.291

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 head0.008
GPT teacher head0.240
Teacher spread0.232 · 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

Citations147
Published2005
Admission routes1
Has abstractyes

Explore more

Same venueThe Journal of Physical Chemistry BSame topicCarbon Nanotubes in CompositesFrench-language works237,207