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Record W2994988925 · doi:10.1063/1.5129665

An apparatus based on an atomic force microscope for implementing tip-controlled local breakdown

2019· article· en· W2994988925 on OpenAlexafffund
Thomas St-Denis, Khadija Yazda, Xavier Capaldi, José Bustamante, Mahdi Safari, Yoichi Miyahara, Y. Zhang, Peter Grütter, Walter Reisner

Bibliographic record

VenueReview of Scientific Instruments · 2019
Typearticle
Languageen
FieldEngineering
TopicNanopore and Nanochannel Transport Studies
Canadian institutionsMcGill UniversityNorcada (Canada)
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health Research
KeywordsNanoporeMaterials scienceNanotechnologySilicon nitrideFabricationBiomoleculeDielectricMembraneSiliconAtomic force microscopyOptoelectronicsElectrolyteElectrodeChemistry

Abstract

fetched live from OpenAlex

Solid-state nanopores are powerful tools for sensing of single biomolecules in solution. Fabrication of solid-state nanopores is still challenging, however; in particular, new methods are needed to facilitate the integration of pores with larger nanofluidic and electronic device architectures. We have developed the tip-controlled local breakdown (TCLB) approach, in which an atomic force microscope (AFM) tip is brought into contact with a silicon nitride membrane that is placed onto an electrolyte reservoir. The application of a voltage bias at the AFM tip induces a dielectric breakdown that leads to the formation of a nanopore at the tip position. In this work, we report on the details of the apparatus used to fabricate nanopores using the TCLB method, and we demonstrate the formation of nanopores with smaller, more controlled diameters using a current limiting circuit that zeroes the voltage upon pore formation. Additionally, we demonstrate the capability of TCLB to fabricate pores aligned to embedded topographical features on the membranes.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.566
Threshold uncertainty score0.886

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.011
GPT teacher head0.262
Teacher spread0.251 · 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

Citations9
Published2019
Admission routes2
Has abstractyes

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