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Record W3160095541 · doi:10.1002/marc.202000514

Macromolecularly Engineered Thermoreversible Heterogeneous Self‐Healable Networks Encapsulating Reactive Multidentate Block Copolymer‐Stabilized Carbon Nanotubes

2021· article· en· W3160095541 on OpenAlexafffund
Ge Zhang, Twinkal Patel, Pothanagandhi Nellepalli, Shubham Bhagat, Hannes Hase, Arman Moini Jazani, Ingo Salzmann, Zhibin Ye, Jung Kwon Oh

Bibliographic record

VenueMacromolecular Rapid Communications · 2021
Typearticle
Languageen
FieldMaterials Science
TopicPolymer composites and self-healing
Canadian institutionsConcordia University
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsCarbon nanotubeMaterials scienceCopolymerCovalent bondNanotechnologyChemical engineeringPolymerPolymer chemistryChemistryComposite materialOrganic chemistry

Abstract

fetched live from OpenAlex

The development of heterogeneous covalent adaptable networks (CANs) embedded with carbon nanotubes (CNTs) that undergo reversible dissociation/recombination through thermoreversibility has been significantly explored. However, the carbon nanotube (CNT)-incorporation methods based on physical mixing and chemical modification could result in either phase separation due to structural incompatibility or degrading conjugation due to a disruption of π-network, thus lowering their intrinsic charge transport properties. To address this issue, the versatility of a macromolecular engineering approach through thermoreversibility by physical modification of CNT surfaces with reactive multidentate block copolymers (rMDBCs) is demonstrated. The formed CNTs stabilized with rMDBCs (termed rMDBC/CNT colloids) bearing reactive furfuryl groups is functioned as a multicrosslinker that reacts with a polymaleimide to fabricate robust heterogeneous polyurethane (PU) networks crosslinked through dynamic Diels-Alder (DA)/retro-DA chemistry. Promisingly, the fabricated PU network gels in which CNTs through rMDBC covalently embedded are flexible and robust to be bendable as well as exhibit self-healing elasticity and enhanced conductivity.

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 score0.153
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.239
Teacher spread0.227 · 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
Published2021
Admission routes2
Has abstractyes

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