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Record W2594488055 · doi:10.1021/acsmacrolett.7b00058

Planet–Satellite Micellar Superstructures Formed by ABCB Terpolymers in Solution

2017· article· en· W2594488055 on OpenAlexafffund
Chao Duan, Weihua Li, Feng Qiu, An‐Chang Shi

Bibliographic record

VenueACS Macro Letters · 2017
Typearticle
Languageen
FieldMaterials Science
TopicBlock Copolymer Self-Assembly
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of China
KeywordsDissipative particle dynamicsSolvophobicPlanetMaterials scienceSatelliteMicelleCopolymerNanotechnologyFabricationChemical engineeringAstrobiologyPhysicsChemistrySolventPolymerOrganic chemistryAqueous solutionComposite materialEngineeringAstronomy

Abstract

fetched live from OpenAlex

The occurrence and relative stability of planet-satellite nanostructures, composed of a host micelle (the planet) accompanied by a number of guest micelles (the satellites), in ABCB tetrablock terpolymer solutions are studied using the polymeric self-consistent field theory and dissipative particle dynamics simulations. The theoretical results demonstrate that the self-assembly of the ABCB tetrablock terpolymers with solvophobic A- and C-blocks and solvophilic B-blocks could lead to the formation of various planet-satellite superstructures, where the planet and satellites are composed of the A- and C-blocks, respectively. Furthermore, the number of satellites is controlled by the ratio of the two B-blocks. The arrangement of the satellites surrounding the planet resembles the solution of the well-known Thomson's problem concerning the optimum arrangement of a given number of electrons on a sphere. Besides providing a facile route to engineering novel multicompartment micelles with planet-satellite superstructures for potential advanced applications, the study strengthens the prospect that multiblock copolymers could become a useful platform for the fabrication of complex nanostructures.

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 score0.007
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.007
GPT teacher head0.228
Teacher spread0.221 · 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

Citations16
Published2017
Admission routes2
Has abstractyes

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