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Record W2335219009 · doi:10.1021/nn201024u

Wall-Selective Probing of Double-Walled Carbon Nanotubes Using Covalent Functionalization

2011· article· en· W2335219009 on OpenAlexaff
Delphine Bouilly, Janie Cabana, François Meunier, Maxime Desjardins-Carrière, François Lapointe, Philippe Gagnon, Francis L. Larouche, Élyse Adam, Matthieu Paillet, Richard Martel

Bibliographic record

VenueACS Nano · 2011
Typearticle
Languageen
FieldMaterials Science
TopicCarbon Nanotubes in Composites
Canadian institutionsRegroupement Québécois sur les Matériaux de PointePolytechnique MontréalUniversité du Québec à MontréalUniversité de Montréal
Fundersnot available
KeywordsSurface modificationCarbon nanotubeMaterials scienceNanotechnologyCovalent bondCoaxialSelective surfaceOptoelectronicsChemical engineeringChemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Double-walled carbon nanotubes (DWNTs) present an original coaxial geometry in which the inner wall is naturally protected from the environment by the outer wall. Covalent functionalization is introduced here as an effective approach to investigate DWNT devices. Performed using an aryldiazonium salt, the functionalization is reversible upon thermal annealing and occurs strictly at the surface of the outer wall, leaving the inner wall essentially unaltered by the chemical bonding. Measurements on functionalized DWNT transistors show that the electrical current is carried by the inner wall and provide unambiguous identification of the metallic or semiconducting character of both walls. New insights about current saturation at high bias in DWNTs are also presented as an illustration of new experiments unlocked by the method. The wall-selectivity of the functionalization not only enables selective optical and electrical probing of the DWNTs, but it also paves the way to designing novel electronic devices in which the inner wall is used for electrical transport while the outer wall chemically interacts with the environment.

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 score0.004
Threshold uncertainty score0.721

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 head0.045
GPT teacher head0.258
Teacher spread0.212 · 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

Citations52
Published2011
Admission routes1
Has abstractyes

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