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Record W2762132669 · doi:10.1002/elps.201700136

Sorting polymers by size via an array of viscous posts

2017· article· en· W2762132669 on OpenAlexafffund
Matthew C. VandeSande, Daniel J. Pasut, Hendrick W. de Haan

Bibliographic record

VenueElectrophoresis · 2017
Typearticle
Languageen
FieldEngineering
TopicNanopore and Nanochannel Transport Studies
Canadian institutionsOntario Tech University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPolymerViscositySortingViscous liquidWork (physics)MicrofluidicsChemical physicsMaterials scienceForcing (mathematics)Range (aeronautics)Langevin dynamicsNanotechnologyMechanicsPhysicsStatistical physicsComputer scienceThermodynamicsComposite materialAlgorithm

Abstract

fetched live from OpenAlex

DNA fragments can be sorted according to size by forcing them through an array of nanoposts. Whereas previous studies have explored solid nanoposts, this work examines nanoposts constructed out of viscous inclusions. Langevin dynamics simulations are used to study the dynamics of polymers driven through arrays of these viscous nanoposts for a range of post viscosities. The results are compared to the solid post case. Increasing post viscosity causes a decrease in the mobility of polymers traversing the array. In the limit of high post viscosity, the mobility becomes lower than in the solid post arrays, rather than converging to it. Analysis of the distributions of event times also shows that the viscous case is fundamentally different from the solid post case. The decrease in mobility in the viscous case arises from slowing down the polymer as it interacts with or even moves through the nanoposts, whereas the solid post case exhibits wrapping and unwrapping dynamics, yielding escape-like statistics. This work suggests that it may be possible to use viscous inclusions within nanofluidic and microfluidic devices to sort biomolecules with high resolution.

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 score0.013
Threshold uncertainty score0.730

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 head0.005
GPT teacher head0.213
Teacher spread0.208 · 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

Citations2
Published2017
Admission routes2
Has abstractyes

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