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Record W4403174086 · doi:10.1039/d4mh01243g

Controlling nano- and microfilament morphology by strategically placing chiral centers in the side chains of bent-core molecules

2024· article· en· W4403174086 on OpenAlexafffund
Ashwathanarayana Gowda, Gourab Acharjee, Suraj Kumar Pathak, Grace A. R. Rohaley, Asmita Shah, Robert P. Lemieux, Marianne E. Prévôt, Torsten Hegmann

Bibliographic record

VenueMaterials Horizons · 2024
Typearticle
Languageen
FieldMaterials Science
TopicDendrimers and Hyperbranched Polymers
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaOhio Department of Higher EducationDivision of Materials ResearchKent State UniversityNational Science Foundation
KeywordsBent molecular geometryCore (optical fiber)MicrofilamentSide chainMoleculeMorphology (biology)NanotechnologyMaterials scienceChemistryPolymerComposite materialCytoskeletonBiologyZoologyOrganic chemistry

Abstract

fetched live from OpenAlex

calculations of molecular conformation, leads to the predictable formation of increasingly complex nano- and microfilament morphologies. Adding to the previously described diversity of twisted and writhed filament types, we here demonstrate and explain the formation and coexistence of flat nanoribbons, nanocylinders, or nano- as well as microfilaments where the morphology spontaneously changes along the filament long axis. For some these more exotic types of filament morphology, helical multilayer filaments suddenly unwind to form flat nanoribbons that also twist again under preservation (not perversion) of the helical twist sense. Moreover, the morphologies formed by this series of molecules now allows us to demonstrate the complete transformation from flat multilayer ribbons over microfilaments and helical-wrapped nanocylinders to helical nanofilaments depending on the number and position of chiral centers in the aliphatic side chains.

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)0.001
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 score0.004
Threshold uncertainty score0.540

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.013
GPT teacher head0.245
Teacher spread0.231 · 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

Citations4
Published2024
Admission routes2
Has abstractyes

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