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Record W1979913737 · doi:10.1103/physreve.61.3003

Small-angle neutron scattering from large unilamellar vesicles: An improved method for membrane thickness determination

2000· article· en· W1979913737 on OpenAlexafffund
Jeremy Pencer, F. R. Hallett

Bibliographic record

VenuePhysical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics · 2000
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicLipid Membrane Structure and Behavior
Canadian institutionsUniversity of Guelph
FundersNatural Sciences and Engineering Research Council of CanadaNational Science Foundation
KeywordsSmall-angle neutron scatteringVesicleScatteringNeutron scatteringSmall-angle scatteringMembraneMaterials scienceSmall-angle X-ray scatteringFunction (biology)Molecular physicsOpticsPhysicsChemistry

Abstract

fetched live from OpenAlex

Small-angle neutron scattering (SANS) measurements were performed on large unilamellar vesicles (LUVs) in order to investigate solute effects on membrane properties. Although SANS is a well established technique for the measurement of membrane thickness in unilamellar vesicles, earlier measurements have depended on approximate treatments of the scattering function and have suffered from effects of multilamellarity or difficulty in sample preparation. More recent studies of temperature induced thickness changes in DPPC LUVs which have included explicit treatment of the full scattering function were complicated by disparities between the predicted and measured scattering curves. Here, we reexamine theoretical descriptions of SANS from LUVs. Motivated by our observations, we then introduce a new method for interpretation of SANS data, which we compare to established techniques and apply to our measurements.

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 categoriesMeta-epidemiology (narrow)
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 score0.139
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.322
Teacher spread0.309 · 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

Citations40
Published2000
Admission routes2
Has abstractyes

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