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Record W2016145920 · doi:10.1002/macp.200900460

Noncovalent Nonspecific Functionalization and Solubilization of Multi‐Walled Carbon Nanotubes at High Concentrations with a Hyperbranched Polyethylene

2009· article· en· W2016145920 on OpenAlexafffund
Lixin Xu, Zhibin Ye, Qingzhou Cui, Zhiyong Gu

Bibliographic record

VenueMacromolecular Chemistry and Physics · 2009
Typearticle
Languageen
FieldMaterials Science
TopicCarbon Nanotubes in Composites
Canadian institutionsLaurentian University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSurface modificationCarbon nanotubeSolubilizationPolymer chemistryPolyethyleneChemistryChemical modificationChemical engineeringMaterials scienceNanotechnologyOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract A hyperbranched polyethylene (HBPE) is employed herein for noncovalent nonspecific functionalization and solubilization of multi‐walled carbon nanotubes (MWCNTs) in organic solvents. Though constructed solely from ethylene without any specific functionality, this unique hyperbranched polymer has been found to effectively solubilize MWCNTs at surprisingly high concentrations (up to 1 235 mg · L−1) in organic solvents such as chloroform and THF. These solubilities are comparable to and even better than the reported best values obtained through noncovalent specific functionalization with conjugated polymers capable of forming specific π‐π interaction with nanotubes in organic solvents. TEM and XRD results confirm that the nanotubes are completely exfoliated and debundled/de‐entangled upon functionalization with HBPE. magnified image

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)0.0010.000

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 head0.007
GPT teacher head0.207
Teacher spread0.200 · 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

Citations56
Published2009
Admission routes2
Has abstractyes

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