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Record W2305616423 · doi:10.1021/acs.macromol.5b02617

Continuous Confinement Fluidics: Getting Lots of Molecules into Small Spaces with High Fidelity

2016· article· en· W2305616423 on OpenAlexafffund
Mohammed Jalal Ahamed, Sara Mahshid, Daniel Berard, François Michaud, Robert Sladek, Walter Reisner, Sabrina Leslie

Bibliographic record

VenueMacromolecules · 2016
Typearticle
Languageen
FieldEngineering
TopicInnovative Microfluidic and Catalytic Techniques Innovation
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchCanada Foundation for Innovation
KeywordsFluidicsNanotechnologyFidelityHigh fidelityMoleculeChemistryMaterials scienceComputer scienceEngineeringAerospace engineeringTelecommunicationsOrganic chemistryElectrical engineering

Abstract

fetched live from OpenAlex

Confining DNA molecules with high throughput and structural integrity is an important challenge in nanochannel-based genomic mapping technology. Here we demonstrate dynamic confinement and linearization of DNA polymers within an embedded nanogroove array with 95% channel occupation. In standard nanofluidic technology, the free energy of confinement experienced by the DNA molecules increases strongly with decreasing device dimensions, leading to a suppression of molecule concentration in nanoconfined spaces. We overcome this limitation by combining “convex lens-induced confinement” (CLiC) geometry with in situ electrophoresis, simultaneously establishing gentle and continuously adjustable nanoconfinement and precise electrokinetic control. Together, these capabilities enable trapping and visualization of extended DNA molecules with high yield over an extended range of conditions. We demonstrate 10-fold increased DNA concentration in a confined region from 10 to 500 nm. Moreover, we develop and validate a predictive model for describing electrokinetically controlled molecule enhancement in continuously varying nanoconfinement. We show that electrophoretic side loading, in continuously varying confinement, not only enhances DNA concentration but also contributes to extend to their full contour length.

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 score0.443
Threshold uncertainty score0.713

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 head0.007
GPT teacher head0.207
Teacher spread0.200 · 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

Citations26
Published2016
Admission routes2
Has abstractyes

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