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Effect of Side Chain Length on Segregation of Squalane between Smectic Layers Formed by Rod-Like Polysilanes

2018· article· en· W2801551519 on OpenAlexvenueno aff
Takuya Tanaka, Itsuki Kato, Kento Okoshi

Bibliographic record

VenueJournal of Research Updates in Polymer Science · 2018
Typearticle
Languageen
FieldMaterials Science
TopicLiquid Crystal Research Advancements
Canadian institutionsnot available
Fundersnot available
KeywordsSqualaneMaterials sciencePolysilanePolymerSmall-angle X-ray scatteringSide chainPhase (matter)ScatteringCrystallographyChemical physicsOpticsComposite materialChemistryOrganic chemistryPhysics

Abstract

fetched live from OpenAlex

The segregation of spherical molecules (squalane) between the smectic layers of rod-like polymers (polysilanes) with narrow molecular weight distributions were investigated by synchrotron radiation small-angle X-ray scattering (SR-SAXS), atomic force microscopy (AFM) observations, and molecular dynamics simulations to elucidate the effect of the polymer side chain length on the segregation. It has been theoretically predicted that the smectic phase of the rod-like particles will be stabilized by inserting the spherical particles into the interstitial region between the smectic layers when the diameter of the spherical particles is smaller than that of the rod-like particles whose length is sufficiently long. We found that the segregation of squalane was unaffected by the molecular weight (Mw) of the polysilane in the range of 9,200-44,100 g/mol, and the diameter of the polysilane showed the optimal size of 5.64 nm for the segregation of squalane whose diameter is 6.57 nm although the origin of these inconsistencies between theory and experiment is currently not clear.

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)0.019
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
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 score0.014
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0190.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.003
Scholarly communication0.0000.001
Open science0.0020.000
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 head0.026
GPT teacher head0.397
Teacher spread0.371 · 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.

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

Citations1
Published2018
Admission routes1
Has abstractyes

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