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Record W2258162445 · doi:10.1021/acs.biomac.5b01216

Interaction Forces between Supported Lipid Bilayers in the Presence of PEGylated Polymers

2015· article· en· W2258162445 on OpenAlexafffund
Xavier Banquy, Dong Woog Lee, Kai Kristiansen, Matthew A. Gebbie, Jacob N. Israelachvili

Bibliographic record

VenueBiomacromolecules · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicLipid Membrane Structure and Behavior
Canadian institutionsUniversité de MontréalUniversité du Québec à Montréal
FundersDivision of Materials ResearchCanadian Institutes of Health ResearchCanada Research Chairs
KeywordsPolymerPoloxamerLipid bilayerChemical engineeringPolyethylene glycolBilayerSurface forces apparatusChemistryLayer (electronics)Polymer chemistryMaterials scienceCopolymerMembraneOrganic chemistry

Abstract

fetched live from OpenAlex

Using the surface forces apparatus (SFA), interaction forces between supported lipid bilayers were measured in the presence of polyethylene glycol and two other commercially available pegylated triblock polymers, Pluronic F68 and F127. Pluronic F68 has a smaller central hydrophobic block compared to F127 and therefore is more hydrophilic. The study aimed to unravel the effects of polymer architecture and composition on the interactions between the bilayers. Our keys findings show that below the critical aggregation concentration (CAC) of the polymers, a soft, weakly anchored, polymer layer is formed on the surface of the bilayers. The anchoring strength of this physisorbed layer was found to increase significantly with the size of the hydrophobic block of the polymer, and was strongest for the more hydrophobic polymer, F127. Above the CAC, a dense polymer layer, exhibiting gel-like properties, was found to rapidly grow on the bilayers even after mechanical disruption. The cohesive interaction maintaining the gel layer structure was found to be stronger for F127, and was also found to promote the formation of highly structured aggregates on the bilayers.

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 score0.005
Threshold uncertainty score0.380

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 head0.025
GPT teacher head0.292
Teacher spread0.267 · 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

Citations15
Published2015
Admission routes2
Has abstractyes

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