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Record W2523809463 · doi:10.1002/mats.201600039

Topological Effect on the Structure of Self‐Assembled Aggregates from Amphiphilic Macromolecules in Solution

2016· article· en· W2523809463 on OpenAlexaff
Yongbing Song, Run Jiang, Zheng Wang, Lang Wang, Yuhua Yin, Baohui Li, An‐Chang Shi

Bibliographic record

VenueMacromolecular Theory and Simulations · 2016
Typearticle
Languageen
FieldChemistry
TopicSurfactants and Colloidal Systems
Canadian institutionsMcMaster University
FundersNational Natural Science Foundation of China
KeywordsAmphiphileMicelleVesicleMacromoleculeCopolymerConfined spaceSelf-assemblyChemistryTopology (electrical circuits)Materials scienceChemical engineeringChemical physicsOrganic chemistryAqueous solutionMembranePolymer

Abstract

fetched live from OpenAlex

The self‐assembled morphologies of cyclic amphiphiles, which are composed of a long hydrophobic block and a short hydrophilic block, in selective solutions are studied by using a simulated annealing method. The morphological dependence of the aggregates on solvent quality is investigated. The topology effects are studied by comparing results from linear counterparts of the amphiphiles. It is observed that, in addition to spherical micelles, cylindrical micelles, disklike micelles, vesicles, and large compound micelles, muticompartment vesicles with several fluidic cores can be formed by the cyclic systems. The morphologies are regulated by the interaction parameter ε AS between the hydrophobic block and solvents. Furthermore, it is revealed that the differences of characteristics of the self‐assembled aggregates originate from the difference in architectural constraint. The wide region of forming multicompartment vesicles suggests that cyclic amphiphilic macromolecules could be a suitable candidate for applications to deliver multiple functional components by compartmentalizing different components in different confined space of vesicles.

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 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 score0.048
Threshold uncertainty score0.749

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 communication0.0000.000
Open science0.0000.000
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 head0.006
GPT teacher head0.224
Teacher spread0.218 · 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.

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

Citations7
Published2016
Admission routes1
Has abstractyes

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