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Record W2806205087 · doi:10.1038/s41598-018-24132-5

Transverse dielectrophoretic-based DNA nanoscale confinement

2018· article· en· W2806205087 on OpenAlexafffund
Sara Mahshid, Jia Grace Lu, Abrar A. Abidi, Robert Sladek, Walter Reisner, Mohammed Jalal Ahamed

Bibliographic record

VenueScientific Reports · 2018
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Bio-sensing Technologies
Canadian institutionsUniversity of WindsorMcGill University
FundersUniversity of WindsorCanadian Institutes of Health ResearchCMC MicrosystemsNatural Sciences and Engineering Research Council of CanadaMcGill University
KeywordsNanoscopic scaleNanotechnologyDNAMaterials scienceComputational biologyChemistryBiologyBiochemistry

Abstract

fetched live from OpenAlex

Abstract Confinement of single molecules within nanoscale environments is crucial in a range of fields, including biomedicine, genomics, and biophysics. Here, we present a method that can concentrate, confine, and linearly stretch DNA molecules within a single optical field of view using dielectrophoretic (DEP) force. The method can convert an open surface into one confining DNA molecules without a requirement for bonding, hydrodynamic or mechanical components. We use a transverse DEP field between a top coverslip and a bottom substrate, both of which are coated with a transparent conductive material. Both layers are attached using double-sided tape, defining the chamber. The nanofeatures lie at the “floor” and do not require any bonding. With the application of an alternating (AC) electric field (2 V p-p ) between the top and bottom electrodes, a DEP field gradient is established and used to concentrate, confine and linearly extend DNA in nanogrooves as small as 100-nm in width. We also demonstrate reversible loading/unloading of DNA molecules into nanogrooves and nanopits by switching frequency (between 10 kHz to 100 kHz). The technology presented in this paper provides a new method for single-molecule trapping and analysis.

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 score0.107
Threshold uncertainty score0.473

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 studies0.0000.001
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 head0.008
GPT teacher head0.193
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

Citations28
Published2018
Admission routes2
Has abstractyes

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