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Record W2081228325 · doi:10.1021/bm4018148

Combined Analysis of Polycation/ODN Polyplexes by Analytical Ultracentrifugation and Dynamic Light Scattering Reveals their Size, Refractive Index Increment, Stoichiometry, Porosity, and Molecular Weight

2014· article· en· W2081228325 on OpenAlexaff
Yves Niebel, Michael D. Buschmann, Marc Lavertu, Grégory De Crescenzo

Bibliographic record

VenueBiomacromolecules · 2014
Typearticle
Languageen
FieldMaterials Science
Topicbiodegradable polymer synthesis and properties
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsDynamic light scatteringAnalytical UltracentrifugationChemistryChitosanMolar mass distributionHydrodynamic radiusStatic light scatteringLight scatteringRefractive indexUltracentrifugeSedimentation coefficientStoichiometryAnalytical Chemistry (journal)PorosityRefractometryChromatographyChemical engineeringScatteringPolymerMaterials sciencePhysical chemistryNanotechnologyOpticsOrganic chemistryNanoparticle

Abstract

fetched live from OpenAlex

Analytical ultracentrifugation (AUC) and dynamic light scattering (DLS) were combined to characterize polyplexes formed with 10 kDa chitosan or 10 kDa PEI and oligodeoxynucleotides (ODN). Combined analysis revealed that both polyplexes were highly porous (over 80%) and that their weight average hydrodynamic diameters were of 46 and 55 nm for chitosan/ODN and PEI/ODN complexes, respectively. Transformation of the sedimentation coefficient distribution to a size and molecular weight distribution gave an average molecular weight of 19 and 29 MDa for chitosan and PEI polyplexes, respectively. Data from AUC also allowed for the calculation of the actual dn/dc and N/P ratios of each polyplex. Additional data from scanning electron microscopy and static light scattering confirmed the conclusions that were initially derived from AUC and DLS, thus validating that the combination of AUC and DLS is a powerful approach to characterize polyplexes in terms of refractive index increment, size, and molecular weight distributions, as well as porosity.

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)0.001
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 score0.051
Threshold uncertainty score0.860

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.223
Teacher spread0.217 · 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

Citations23
Published2014
Admission routes1
Has abstractyes

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