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Record W2728101226 · doi:10.1002/pola.28652

Dispersion of single‐walled carbon nanotubes using nucleobase‐containing poly(acrylamide) polymers

2017· article· en· W2728101226 on OpenAlexaff
Darryl Fong, Zan Hua, Thomas R. Wilks, Rachel K. O’Reilly, Alex Adronov

Bibliographic record

VenueJournal of Polymer Science Part A Polymer Chemistry · 2017
Typearticle
Languageen
FieldMaterials Science
TopicCarbon Nanotubes in Composites
Canadian institutionsMcMaster University
Fundersnot available
KeywordsCarbon nanotubeNucleobasePolymerMaterials scienceCopolymerDispersion (optics)Chemical engineeringSurface modificationNanotubePolymer chemistryNanotechnologyChemistryDNAComposite material

Abstract

fetched live from OpenAlex

ABSTRACT Single‐walled carbon nanotubes (SWNTs) possess extraordinary properties, but suffer from poor solubility and a lack of purity. Of the possible routes available to solubilize and purify nanotube samples, the use of noncovalent functionalization is ideal as carbon nanotube properties are not deleteriously affected. A multitude of different dispersants have been investigated thus far, but of particular interest is deoxyribonucleic acid (DNA), which has previously been demonstrated to effectively separate metallic and semiconducting carbon nanotubes. Here, we investigate the ability of synthetic nucleobase‐containing poly(acrylamide) polymers to produce stable nanotube dispersions in organic solvents. Polymers bearing different nucleobase and backbone structures, as well as block copolymers with different block sequences were investigated. Polymer:SWNT mass ratios and solvent compositions were optimized for the nucleobase‐functionalized polymers, and semiconducting and metallic SWNT populations were identified by a combination of UV‐Vis‐NIR absorption, Raman, and fluorescence spectroscopy. These results demonstrate the capacity for synthetic DNA analogues to disperse SWNTs in organic media. © 2017 Wiley Periodicals, Inc. J. Polym. Sci., Part A: Polym. Chem. 2017 , 55 , 2611–2617

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
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 score0.016
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.003
Scholarly communication0.0010.001
Open science0.0030.001
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 head0.022
GPT teacher head0.275
Teacher spread0.253 · 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.

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

Citations8
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Polymer Science Part A Polymer ChemistrySame topicCarbon Nanotubes in CompositesFrench-language works237,207