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Record W4393180665 · doi:10.3390/nano14070572

Nanoscale Structure of Lipid–Gemini Surfactant Mixed Monolayers Resolved with AFM and KPFM Microscopy

2024· article· en· W4393180665 on OpenAlexafffund
Robert D. E. Henderson, Nanqin Mei, Yue Xu, Ravi Gaikwad, Shawn Wettig, Zoya Leonenko

Bibliographic record

VenueNanomaterials · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicLipid Membrane Structure and Behavior
Canadian institutionsUniversity of Waterloo
FundersWaterloo Institute for Nanotechnology, University of WaterlooNatural Sciences and Engineering Research Council of CanadaGovernment of Ontario
KeywordsMonolayerNanoscopic scaleKelvin probe force microscopePulmonary surfactantAtomic force microscopyMicroscopyChemistryNanotechnologyGene deliveryMaterials scienceDrug deliveryBiophysicsAnalytical Chemistry (journal)Organic chemistryTransfectionOpticsGeneBiochemistry

Abstract

fetched live from OpenAlex

Drug delivery vehicles composed of lipids and gemini surfactants (GS) are promising in gene therapy. Tuning the composition and properties of the delivery vehicle is important for the efficient load and delivery of DNA fragments (genes). In this paper, we studied novel gene delivery systems composed of 1,2-dioleoyl-sn-glycero-3-phosphocholine (DOPC), 1,2-dipalmitoyl-sn-3-phosphocholine (DPPC), and GS of the type N,N-bis(dimethylalkyl)-α,ω-alkanediammonium dibromide at different ratios. The nanoscale properties of the mixed DOPC–DPPC–GS monolayers on the surface of the gene delivery system were studied using atomic force microscopy (AFM) and Kelvin probe force microscopy (KPFM). We demonstrate that lipid–GS mixed monolayers result in the formation of nanoscale domains that vary in size, height, and electrical surface potential. We show that the presence of GS can impart significant changes to the domain topography and electrical surface potential compared to monolayers composed of lipids alone.

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 score0.014
Threshold uncertainty score0.642

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 head0.239
Teacher spread0.233 · 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

Citations3
Published2024
Admission routes2
Has abstractyes

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