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Record W2887953676 · doi:10.1002/anie.201807379

Hierarchical Self‐Assembly in Liquid‐Crystalline Block Copolymers Enabled by Chirality Transfer

2018· article· en· W2887953676 on OpenAlexaff
Shuai Huang, Yuxuan Chen, Shudeng Ma, Haifeng Yu

Bibliographic record

VenueAngewandte Chemie International Edition · 2018
Typearticle
Languageen
FieldMaterials Science
TopicLiquid Crystal Research Advancements
Canadian institutionsMinistry of Education and Child Care
FundersNational Natural Science Foundation of China
KeywordsAzobenzeneChirality (physics)Materials scienceCopolymerAmphiphileSelf-assemblyEnantiopure drugPolymerEthylene oxideNanostructureLiquid crystalDopantNanotechnologyChemical engineeringPolymer chemistryDopingOrganic chemistryChemistryEnantioselective synthesisComposite materialChiral symmetry breaking

Abstract

fetched live from OpenAlex

Helical topological structures are often found in chiral biological systems, but seldom in synthesized polymers. Now, controllable microphase separation of amphiphilic liquid-crystalline block copolymers (LCBCs) consisting of hydrophilic poly(ethylene oxide) and hydrophobic azobenzene-containing poly(methylacrylate) is combined with chirality transfer to fabricate helical nanostructures by doping with chiral additives (enantiopure tartaric acid). Through hydrogen-bonding interactions, chirality is transferred from the dopant to the aggregation, which directs the hierarchical self-assembly in the composite system. Upon optimized annealing condition, helical structures in film are fabricated by the induced aggregation chirality. The photoresponsive azobenzene mesogens in the LCBC assist photoregulation of the self-assembled helical morphologies. This allows the construction and non-contact manipulation of complicated 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 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 score0.002

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)0.0010.000

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 head0.015
GPT teacher head0.291
Teacher spread0.276 · 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

Citations90
Published2018
Admission routes1
Has abstractyes

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