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Record W2766380923 · doi:10.1002/anie.201709129

Direct Synthesis of Polymer Nanotubes by Aqueous Dispersion Polymerization of a Cyclodextrin/Styrene Complex

2017· article· en· W2766380923 on OpenAlexaff
Xi Chen, Lei Liu, Meng Huo, Min Zeng, Peng Liao, Anchao Feng, Xiaosong Wang, Jinying Yuan

Bibliographic record

VenueAngewandte Chemie International Edition · 2017
Typearticle
Languageen
FieldChemistry
TopicAdvanced Polymer Synthesis and Characterization
Canadian institutionsUniversity of Waterloo
FundersNational Natural Science Foundation of China
KeywordsPolystyreneCopolymerMaterials sciencePolymerizationNanotubeStyreneMonomerPolymerPolymer chemistryCyclodextrinDispersion (optics)Dispersion polymerizationChemical engineeringNanomaterialsAmphiphileAqueous solutionNanostructureNanotechnologyChemistryCarbon nanotubeOrganic chemistryComposite material

Abstract

fetched live from OpenAlex

A one-step synthesis of nanotubes by RAFT dispersion polymerization of cyclodextrin/styrene (CD/St) complexes directly in water is presented. The resulted amphiphilic PEG-b-PS diblock copolymers self-assemble in situ into nanoparticles with various morphologies. Spheres, worms, lamellae, and nanotubes were controllably obtained. Because of the complexation, the swelling degree of polystyrene (PS) blocks by free St is limited, resulting in limited mobility of PS chains. Consequently, kinetically trapped lamellae and nanotubes were obtained instead of spherical vesicles. During the formation of nanotubes, small vesicles first formed at the ends of the tape-like lamellae, then grew and fused into nanotubes with a limited chain rearrangement. The introduction of a host-guest interaction based on CDs enables the aqueous dispersion polymerization of water-immiscible monomers, and produces kinetically trapped nanostructures, which could be a powerful technique for nanomaterials synthesis.

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 score0.003

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)0.0010.001

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 head0.013
GPT teacher head0.251
Teacher spread0.239 · 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

Citations150
Published2017
Admission routes1
Has abstractyes

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