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Record W2033295829 · doi:10.1021/la034727n

Nanoscale Stripe Patterns in Phospholipid Bilayers Formed by the Langmuir−Blodgett Technique

2003· article· en· W2033295829 on OpenAlexaff
Patricia Moraille, Antonella Badia

Bibliographic record

VenueLangmuir · 2003
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicLipid Membrane Structure and Behavior
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsMonolayerMicaBilayerPhospholipidLangmuir–Blodgett filmPhase (matter)AdsorptionChemical engineeringDeposition (geology)Surface forces apparatusMembraneMaterials scienceNanoscopic scaleLipid bilayerChemistryNanotechnologyOrganic chemistryComposite material

Abstract

fetched live from OpenAlex

A new methodology has been developed to create an extensive pattern of parallel stripes, ∼150−250 nm wide, in phospholipid bilayers supported on mica. These striped bilayers are prepared by the Langmuir−Blodgett (LB) film technique. A striped monolayer consisting of two phospholipids in different states (condensed and liquid-expanded) is used to direct the deposition of the solid- and liquidlike phases of a second mixed monolayer during LB transfer. We also demonstrate that bilayer stripes can be generated by the condensation of phospholipids over the solidlike stripe domains of the underlying monolayer for a one-component film deposited just below the liquid-expanded-to-liquid-condensed phase transition pressure. Nonionic detergent extraction of the liquidlike phase from these LB films resulted in bilayer-thick phospholipid stripes separated by a mica surface. A periodic array of grooves was produced by the selective adsorption of protein onto the mica regions of the detergent-treated bilayer. The LB film deposition of binary mixtures of solid-phase- and fluid-phase-forming phospholipids constitutes a novel strategy to create linear surface patterns that can be used to direct the deposition of molecules.

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 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 score0.169
Threshold uncertainty score0.483

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.006
GPT teacher head0.231
Teacher spread0.225 · 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

Citations39
Published2003
Admission routes1
Has abstractyes

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