MétaCan
Menu
Back to cohort
Record W2023095153 · doi:10.1021/ja8036788

To Dope or Not To Dope: The Effect of Sonicating Single-Wall Carbon Nanotubes in Common Laboratory Solvents on Their Electronic Structure

2008· article· en· W2023095153 on OpenAlexaff
Kevin R. Moonoosawmy, Peter Kruse

Bibliographic record

VenueJournal of the American Chemical Society · 2008
Typearticle
Languageen
FieldMaterials Science
TopicCarbon Nanotubes in Composites
Canadian institutionsMcMaster University
Fundersnot available
KeywordsChemistryDichloromethaneSonicationCarbon nanotubeSolventRaman spectroscopyDopantChloroformMoleculeCatalysisInorganic chemistryX-ray photoelectron spectroscopyHydrogen chlorideChemical engineeringPhotochemistryDopingOrganic chemistryMaterials science

Abstract

fetched live from OpenAlex

Single-wall carbon nanotubes (SWCNTs) are commonly dispersed via sonication in a solvent prior to functionalization. We show that solvents such as dichloromethane, chloroform, 1,2-dichloroethane, and o-dichlorobenzene lead to an upward shift in the Raman response of the SWCNTs. We have used o-dichlorobenzene as a model molecule to explain this effect, and an upward shift of 9 cm(-1) is observed in the D* band. This blue shift is associated with p-type doping and is triggered only when the nanotubes are sonicated in the solvent. Sonication decomposes the chlorinated solvents, and new species (Cl2 and HCl(g)) are formed. The catalytic Fe nanoparticles inherently present in the nanotubes are etched by chlorine and hydrogen chloride to form iron chlorides during sonication in the solvent. The dopant was identified by X-ray photoelectron spectroscopy. With such knowledge of doping, the choice of solvent becomes crucial for any chemical reaction and can be intentionally tuned to produce SWCNTs films for electronics applications.

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 score0.003
Threshold uncertainty score0.507

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.009
GPT teacher head0.258
Teacher spread0.249 · 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

Citations78
Published2008
Admission routes1
Has abstractyes

Explore more

Same venueJournal of the American Chemical SocietySame topicCarbon Nanotubes in CompositesFrench-language works237,207