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Record W2949323417 · doi:10.1002/anie.201904749

Breaking Parallel Orientation of Rods via a Dendritic Architecture toward Diverse Supramolecular Structures

2019· review· en· W2949323417 on OpenAlexaff
Ruimeng Zhang, Xueyan Feng, Rui Zhang, Wenpeng Shan, Zebin Su, Jialin Mao, Chrys Wesdemiotis, Jiahao Huang, Xiaoyun Yan, Tong Liu, Tao Li, Mingjun Huang, Zhiwei Lin, An‐Chang Shi, Stephen Z. D. Cheng

Bibliographic record

VenueAngewandte Chemie International Edition · 2019
Typereview
Languageen
FieldMaterials Science
TopicSupramolecular Self-Assembly in Materials
Canadian institutionsMcMaster University
FundersNational Natural Science Foundation of ChinaU.S. Department of EnergyNational Science Foundation
KeywordsRodSupramolecular chemistryMoleculeMaterials scienceSelf-assemblyLiquid crystalCrystallographyChemical physicsOrientation (vector space)Soft matterNanostructureNanotechnologyGeometryChemistryCrystal structureOptoelectronics

Abstract

fetched live from OpenAlex

Self-assembled nanostructures of rod-like molecules are commonly limited to nematic or layered smectic structures dominated by the parallel arrangement of the rod-like components. Distinct self-assembly behavior of four categories of dendritic rods constructed by placing a tri(hydroxy) group at the apex of dendritic oligo-fluorenes is observed. Designed hydrogen bonding and dendritic architecture break the parallel arrangement of the rods, resulting in molecules with specific (fan-like or cone-like) shapes. While the fan-shaped molecules tend to form hexagonal packing cylindrical phases, the cone-shaped molecules could form spherical motifs to pack into various ordered structures, including the Frank-Kasper A15 phase and dodecagonal quasicrystal. This study provides a model system to engineer diverse supramolecular structures by rod-like molecules and sheds new light into the mechanisms of the formation of unconventional spherical packing structures in soft matter.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.037
GPT teacher head0.329
Teacher spread0.292 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations33
Published2019
Admission routes1
Has abstractyes

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