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Record W1995401006 · doi:10.1021/la0514922

Controlled Chemical Functionalization of Multiwalled Carbon Nanotubes by Kiloelectronvolt Argon Ion Treatment and Air Exposure

2005· article· en· W1995401006 on OpenAlexaff
De‐Quan Yang, Jean-Francois Rochette, E. Sacher

Bibliographic record

VenueLangmuir · 2005
Typearticle
Languageen
FieldMaterials Science
TopicCarbon Nanotubes in Composites
Canadian institutionsPolytechnique MontréalRegroupement Québécois sur les Matériaux de Pointe
Fundersnot available
KeywordsSurface modificationCarbon nanotubeArgonChemical modificationNanotechnologyCarbon fibersChemical engineeringChemistryIonMaterials scienceOrganic chemistryPolymer chemistryPhysical chemistryComposite material

Abstract

fetched live from OpenAlex

The chemical and morphological modifications of multiwalled carbon nanotubes (MWCNTs), by 2 keV Ar(+) treatment, have been followed by field emission scanning (FESEM) and high-resolution transmission (HRTEM) electron microscopies and by X-ray photoelectron (XPS) and Raman spectroscopies. Morphological changes were followed, both in situ and on subsequent air exposure, and the data indicate that free radical defects, initially produced under low Ar(+) treatment doses ( approximately 10(13) ions/cm(2)), act as the nuclei for the formation of localized asperities that form along the walls of the CNTs. Continued treatment results in their stublike elongation that continues with further treatment, forming extensions under heavy treatment doses. The chemical changes that occur, on reaction with air, reveal that the defects initially created are secondary C atoms, formed when a single bond breaks; further treatment breaks an additional bond to form primary C atoms; free radical fragments, lost when the third bond breaks, condense on the free radical defects to form the asperities. The extent of primary and secondary C atoms, and thus their functionalization on air exposure, may be controlled by the extent of treatment, offering a method for the controlled surface functionalization of CNTs by low-energy Ar(+) treatment.

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 score0.007
Threshold uncertainty score0.545

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 head0.005
GPT teacher head0.215
Teacher spread0.210 · 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

Citations71
Published2005
Admission routes1
Has abstractyes

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