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Record W2794155290 · doi:10.1002/marc.201700783

Hydrogen Bond Induces Hierarchical Self‐Assembly in Liquid‐Crystalline Block Copolymers

2018· article· en· W2794155290 on OpenAlexaff
Shuai Huang, Linlin Pang, Yuxuan Chen, Liming Zhou, Shaoming Fang, Haifeng Yu

Bibliographic record

VenueMacromolecular Rapid Communications · 2018
Typearticle
Languageen
FieldMaterials Science
TopicBlock Copolymer Self-Assembly
Canadian institutionsMinistry of Education and Child Care
FundersNational Natural Science Foundation of China
KeywordsMaterials scienceLamellar structureCopolymerEthylene oxideDopantHydrogen bondSelf-assemblyCrystallizationAmorphous solidPolymer chemistryChemical engineeringAmphiphilePolymerPhase (matter)Liquid crystalMoleculeDopingNanotechnologyCrystallographyOrganic chemistryChemistryComposite material

Abstract

fetched live from OpenAlex

Microphase-separated structures of block copolymers (BCs) with a size of sub-10 nm are usually obtained by hydrogen-bond-induced self-assembly of BCs through doping with small molecules as functional additives. Here, fabrication of hierarchically self-assembled sub-10 nm structures upon microphase separation of amphiphilic liquid-crystalline BCs (LCBCs) at the existence of hydrogen bonds but without any dopants is reported. The newly introduced urethane groups in the side chain of the hydrophobic block of LCBCs interact with the ether groups of the hydrophilic poly(ethylene oxide) (PEO) block, leading to imperfect crystallization of the PEO blocks. Both crystalline and amorphous domains coexist in the separated PEO phase, enabling a lamellar structure to appear inside the PEO nanocylinders. This provides an elegant method to fabricate controllable sub-10 nm microstructures in well-defined polymer systems without the introduction of any dopants.

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 head0.016
GPT teacher head0.269
Teacher spread0.254 · 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

Citations16
Published2018
Admission routes1
Has abstractyes

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