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Record W4414285899 · doi:10.1126/sciadv.adv8863

Single-molecule capture, release, and dynamical manipulation via reversible electrokinetic confinement (RECON)

2025· article· en· W4414285899 on OpenAlexaff
Matheus Azevedo Silva Pessôa, Piotr Jakuc, Carolina Martins e Queiroz, Naomi Duggan, Seraphine Kautz, Zezhou Liu, Preethi Ravikumar, Sajad Shiekh, Han Cao, Sara Mahshid, Deborah Kuchnir Fygenson, Walter Reisner

Bibliographic record

VenueScience Advances · 2025
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Bio-sensing Technologies
Canadian institutionsMcGill University
Fundersnot available
KeywordsElectrokinetic phenomenaNanofluidicsFlexibility (engineering)ElectrodeMicrofluidicsDielectricVoltageGating

Abstract

fetched live from OpenAlex

We present a nanofluidic device enabling single-molecule confinement through free-energy landscapes created by dynamic electrical gating of embedded nanoelectrodes. Unlike static geometric confinement, this system uses a parallel electrode configuration with nanoelectrodes placed in a dielectric layer. Localized electrokinetic fields at electrode wells form tunable attractive potential wells for bimolecular capture. By modulating the voltage bias waveform, the device allows precise control over confinement dynamics, enabling molecular capture, release, and exposure to periodic or stochastic confinement regimes. This flexibility facilitates the study of biomolecular behavior under dynamically adjustable conditions, including controlled confinement fluctuations. The device can manipulate diverse analytes such as double-stranded DNA, liposomes, and DNA nanotubes and facilitates introducing molecules into confined environments intact from bulk while providing enhanced tunability. With the ability to implement tailored confinement profiles, this platform represents a versatile tool for probing molecular confinement and behavior in complex, dynamically varying environments.

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 score0.094
Threshold uncertainty score0.334

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 head0.006
GPT teacher head0.217
Teacher spread0.211 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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