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Record W1974227720 · doi:10.1117/12.821391

Dispersion of single-walled carbon nanotubes using polyelectrolytes

2009· article· en· W1974227720 on OpenAlexafffund
Gabriela Aldea, Jean‐Michel Nunzi

Bibliographic record

VenueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2009
Typearticle
Languageen
FieldMaterials Science
TopicCarbon Nanotubes in Composites
Canadian institutionsQueen's University
FundersCanada Research Chairs
KeywordsCarbon nanotubeSurface modificationMaterials sciencePolyelectrolyteDispersion (optics)Maleic anhydrideDispersion stabilityCopolymerNanotechnologyChemical engineeringNanoparticlePolymerComposite material

Abstract

fetched live from OpenAlex

In order to produce high performance SWNT-based products, it is necessary to make them soluble, reaching a certain degree of dispersion and stability in solution. Since SWNTs are mostly inert, being neither hydrophilic nor lipophilic, their use suffers from poor dispersion capability and weak interaction with other partners. Therefore, activating and modifying their surface is an essential prerequisite to processing. We report on a versatile nondestructive strategy for the non-covalent functionalization of SWNT by polyelectrolytes based on maleic anhydride copolymers. To evaluate competing stabilization characteristics, we explored the dispersing power of a range of maleic anhydride copolymers functionalized with several chromophore units: pyrene, cholesterol and Disperse Red 1. The surface modification of SWNT is straightforward and efficient for making them dispersible in water and in other organic solvents and for producing nanometer-scale materials suitable for nanotechnology, medicinal chemistry and environment friendly solar cell 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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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 head0.012
GPT teacher head0.229
Teacher spread0.217 · 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 source (direct Gemma or distilled Codex), 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

Citations1
Published2009
Admission routes2
Has abstractyes

Explore more

Same venueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIESame topicCarbon Nanotubes in CompositesFrench-language works237,207