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Record W2330642423 · doi:10.1021/jp502806v

Form Factor of Asymmetric Elongated Micelles: Playing with Russian Dolls Has Never Been so Informative

2014· article· en· W2330642423 on OpenAlexafffund
Gérald Guérin, Graeme Cambridge, Mohsen Soleimani, Sepehr Mastour Tehrani, Ian Manners, Mitchell A. Winnik

Bibliographic record

VenueThe Journal of Physical Chemistry B · 2014
Typearticle
Languageen
FieldMaterials Science
TopicSupramolecular Self-Assembly in Materials
Canadian institutionsUniversity of Toronto
FundersEuropean Research CouncilNatural Sciences and Engineering Research Council of Canada
KeywordsRodScatteringDispersityForm factor (electronics)Structure factorAsymmetryNeutron scatteringOpticsMicelleSmall-angle neutron scatteringPhysicsMaterials scienceCondensed matter physicsChemistryQuantum mechanicsPolymer chemistryPhysical chemistry

Abstract

fetched live from OpenAlex

Scattering techniques (i.e., static light scattering, small angle neutron scattering,11 or small angle X-ray scattering) are excellent tools to study nanoscopic objects in solution. However, to interpret the experimental data, one needs to use the appropriate form factor. While recent progress has been made in the writing of form factors for complex structures, there is still a need to develop a method to evaluate the form factor of inhomogeneous elongated scatterers. Here, we propose an approach based on the principle of "Russian dolls". Multiblock rods are represented as multi generations of rods (mother, daughter, granddaughter, etc.), where each rod is nested within the rod of the previous generation, like Russian dolls. A shift parameter is used to introduce asymmetry in the rod along its long axis. This approach not only allowed us to write the form factor of multiblock rods, but it also gave us the possibility to account for the polydispersity in length of each block and of the shift parameter. Finally, we applied these equations to the case of a series of solutions of triblock comicelles slightly polydisperse in length.

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 score0.002
Threshold uncertainty score0.495

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 science0.0010.000
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 head0.010
GPT teacher head0.232
Teacher spread0.222 · 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
Published2014
Admission routes2
Has abstractyes

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