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Record W2021378679 · doi:10.1021/ma000448k

Electrophoresis of Composite Molecular Objects. 2. Competition between Sieving and Frictional Effects in Polymer Solutions

2001· article· en· W2021378679 on OpenAlexafffund
Claude Desruisseaux, Guy Drouin, Gary W. Slater

Bibliographic record

VenueMacromolecules · 2001
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Capillary Electrophoresis Applications
Canadian institutionsUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsElectrophoresisPolymerChemistryMoleculeCapillary electrophoresisGel electrophoresis of nucleic acidsStreptavidinGel electrophoresisDNAChemical physicsPolyacrylamideChromatographyPolymer chemistryOrganic chemistryBiotinBiochemistry

Abstract

fetched live from OpenAlex

We previously showed that labeling one end of single-stranded DNA molecules with a neutral label like streptavidin increases the interband separation of these hybrid DNA molecules when they are electrophoresed in denaturing cross-linked polyacrylamide gels. This separation process is called trapping electrophoresis because the dynamical properties of the analytes are strongly affected by random steric trapping in the gel. End-labeled ssDNA molecules can also be electrophoretically separated in free solution. This process is called end-labeled free solution electrophoresis, and the separation is then due to the extra hydrodynamic friction provided by the streptavidin label. In this article, we present a study of the capillary electrophoresis of DNA molecules end-labeled with streptavidin in the presence of non-cross-linked polymer solutions ranging from semidilute to ultradilute conditions. In the semidilute limit, sieving interactions dominate, and small DNA molecules move faster than larger ones. In the ultradilute limit, however, we recover free-solution-like separations where small molecules are more retarded than larger ones. We also observe a fascinating transition between these two regimes, for intermediate polymer concentrations and DNA sizes, where the competition between trapping, sieving, and frictional effects leads to nonmonotonic mobility−size relationships. Our theoretical analysis suggests a universal relationship between the mobility of labeled and unlabeled DNA molecules in all electrophoretic conditions.

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 score0.070
Threshold uncertainty score0.702

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 head0.004
GPT teacher head0.190
Teacher spread0.186 · 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

Citations17
Published2001
Admission routes2
Has abstractyes

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