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Record W3021821953 · doi:10.1021/acsapm.0c00278

Reactive Multidentate Block Copolymer Stabilization to Carbon Nanotubes for Thermoreversible Cross-Linked Network Gels

2020· article· en· W3021821953 on OpenAlexafffund
Ge Zhang, Jung Kwon Oh

Bibliographic record

VenueACS Applied Polymer Materials · 2020
Typearticle
Languageen
FieldMaterials Science
TopicPolymer composites and self-healing
Canadian institutionsConcordia University
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsCopolymerNanomaterialsPyreneCarbon nanotubeMaterials sciencePolymerDenticityChemical engineeringNanoparticleSurface modificationCarbon fibersNanotechnologyPolymer chemistryComposite numberChemistryOrganic chemistryComposite material

Abstract

fetched live from OpenAlex

Control over surface chemistry is essential for various applications of colloidal carbon-based and inorganic nanomaterials. Here, a reactive multidentate block copolymer (rMDBC) strategy is demonstrated with the synthesis of rMDBC composed of a furfuryl block designed to react with maleimides through a Diels–Alder (DA) reaction and a pyrene block designed to bind to carbon materials through a π–π interaction. The synthesized rMDBC enables the stabilization of carbon nanotube (CNT) surfaces to form colloidally stable rMDBC/CNT colloids, and it has a greater binding affinity to CNTs compared with those of its counterparts, such as a homopolymer bearing pendant pyrene groups and a monodentate homopolymer bearing a pyrene terminal group. Furthermore, the resultant colloids bearing multiple furfuryl groups are highly applicable as reactive cross-linkers for the fabrication of thermally induced cross-linked networks exhibiting thermoreversibility with a dimaleimide. These results suggest that the rMDBC strategy is an effective platform for the stabilization of nanomaterial surfaces in single layers and, thus, the development of high performance, dynamic, cross-linked self-healable materials.

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 categoriesMeta-epidemiology (narrow)
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 score0.002
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.022
GPT teacher head0.267
Teacher spread0.246 · 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

Citations6
Published2020
Admission routes2
Has abstractyes

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