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Record W2083857432 · doi:10.1021/la102603a

Interaction between Pluronic F127 and Dioctadecyldimethylammonium Bromide (DODAB) Vesicles Studied by Differential Scanning Calorimetry

2010· article· en· W2083857432 on OpenAlexaff
Eloi Feitosa, Françoise M. Winnik

Bibliographic record

VenueLangmuir · 2010
Typearticle
Languageen
FieldChemistry
TopicSurfactants and Colloidal Systems
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsDifferential scanning calorimetryPoloxamerVesicleChemistryEthylene oxideMicellePropylene oxideBromideChemical engineeringIsothermal titration calorimetryBilayerPolymerAqueous solutionPolymer chemistryChromatographyCopolymerOrganic chemistryMembraneThermodynamicsPhysical chemistry

Abstract

fetched live from OpenAlex

A number of fundamental studies on the interactions between lipid bilayers and (ethylene oxide)-b-(propylene oxide)-b-(ethylene oxide) copolymers (PEO-PPO-PEO, Pluronics) have been carried out recently as model systems for the complex behavior of cell membranes with this class of polymers often employed in pharmaceutical formulations. We report here a study by differential scanning calorimetry (DSC) of the interactions in water between Pluronic F127 (F127), and the cationic vesicles of di-n-octadecyldimethylammonium bromide (DODAB), as a function of concentration of the two components (DODAB 0.1 and 1.0 mM; F127 0.1 to 5.0 mM) and of the sample preparation protocol. The DSC studies follow the critical micellization temperature (cmt ≈ 27 °C at 1.0 mM) of F127 and the gel-liquid crystal transition (T(m) ≈ 45 °C) of the DODAB bilayer and of F127/DODAB mixtures. Upon heating past T(m), vesicle/polymer mixtures undergo an irreversible conversion into mixed DODAB/F127 micelles and/or F127-bearing vesicles, depending on the relative amount of each component, together with, in some cases, residual intact F127 micelles or DODAB vesicles. Sample preparation protocol is shown to have little impact on the composition of mixed systems once they are heated above T(m).

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 score0.095
Threshold uncertainty score0.779

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 head0.012
GPT teacher head0.252
Teacher spread0.240 · 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

Citations27
Published2010
Admission routes1
Has abstractyes

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