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Record W4405706081 · doi:10.1002/anie.202419042

Liquid Crystalline Nanorods by Synchronized Polymerization, Self‐Assembly and Oriented Attachment for Utilization in Magnetically Responsive Displays

2024· article· en· W4405706081 on OpenAlexaff
Xiao Wang, Jiawei Lu, An‐Chang Shi, Bing Liu

Bibliographic record

VenueAngewandte Chemie International Edition · 2024
Typearticle
Languageen
FieldMaterials Science
TopicLiquid Crystal Research Advancements
Canadian institutionsMcMaster University
FundersNational Natural Science Foundation of China
KeywordsNanorodPolymerizationMaterials scienceNanotechnologyNanomaterialsSelf-assemblyLiquid crystalNanoparticlePolymerChemical engineeringOptoelectronics

Abstract

fetched live from OpenAlex

The creation of anisotropic nanoparticles (NPs) by polymerization and/or self-assembly (SA) has significantly promoted the applications of polymer nanomaterials in many fields. However, polymer nanorods are not easily accessible via conventional polymerization or SA. Here we report a one-step route to synthesize single-domain smectic liquid crystalline (LC) nanorods utilizing oriented attachment (OA) that was usually found in the synthesis of inorganic NPs, synchronized with polymerization and SA. The synchronization was achieved by developing a novel stabilizer derived from a thermo-responsive polyelectrolyte system. Mechanistic studies reveal that controlling the thermo-responsive behavior and the distribution of stabilizers on NPs enabled OA. The LC nanorods can further form hierarchical colloidal LCs, which show much larger light transmittance than that of non-LC nanorods. Moreover, we demonstrate that this LC system can be manipulated by an external magnetic field, thus providing a candidate material for magnetic-responsive display.

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 score0.294
Threshold uncertainty score0.685

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 science0.0000.000
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 head0.015
GPT teacher head0.321
Teacher spread0.306 · 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

Citations8
Published2024
Admission routes1
Has abstractyes

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