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Record W2047501651 · doi:10.1021/ja038653d

Distribution and Modification of Sorption Sites in Amphiphilic Calixarene-Based Solid Lipid Nanoparticles from Hyperpolarized<sup>129</sup>Xe NMR Spectroscopy

2004· article· en· W2047501651 on OpenAlexaff
Alix Dubes, Igor Moudrakovski, Patrick Shahgaldian, Anthony W. Coleman, Christopher I. Ratcliffe, John A. Ripmeester

Bibliographic record

VenueJournal of the American Chemical Society · 2004
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicLipid Membrane Structure and Behavior
Canadian institutionsSteacie Institute for Molecular Sciences
Fundersnot available
KeywordsChemistryAmphiphileCalixareneNuclear magnetic resonance spectroscopyMoleculeSorptionAdsorptionXenonLipid bilayerMembraneVesicleNanoparticleSpectroscopyChemical engineeringOrganic chemistryCopolymer

Abstract

fetched live from OpenAlex

The site distribution and accessibility in amphiphilic calixarenes-based solid lipid nanoparticles were determined as a function of lipid chain length using in situ 129Xe NMR spectroscopy with flowing hyperpolarized Xe gas. The study illustrates that host cavities in as-prepared materials are increasingly occluded by the lipid chain for compounds with chain lengths from C6 to C12 and are almost completely occluded for C14 and C16 chain lengths. Host cavities present at the surface of the particles are still accessible to small atoms (xenon) and organic molecules (methylene chloride, etc). The Xe spectra show that the accessible void space can be increased remarkably by exposure of the particle surface to suitably sized guest molecules that appear to displace the occluding hydrocarbon chains from the host cavities by competitive adsorption. This postsynthesis treatment thus modifies the state of self-assembly and improves sorption capability. The HP Xe NMR approach presented is suitable for small samples (a few milligrams) of SLNs, likely also for other biomaterials such as vesicles, model membranes, etc.

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 score0.003
Threshold uncertainty score0.359

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 head0.009
GPT teacher head0.256
Teacher spread0.247 · 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

Citations33
Published2004
Admission routes1
Has abstractyes

Explore more

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