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Record W3092050493 · doi:10.1063/5.0021739

Consistent probe spacing in multi-probe STM experiments

2020· article· en· W3092050493 on OpenAlexafffund
Jo Onoda, D. Vick, Mark Salomons, Robert A. Wolkow, Jason Pitters

Bibliographic record

VenueAIP Advances · 2020
Typearticle
Languageen
FieldPhysics and Astronomy
TopicSurface and Thin Film Phenomena
Canadian institutionsNational Institute for NanotechnologyUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaAlberta Innovates - Technology FuturesNational Research Council CanadaCompute Canada
KeywordsFocused ion beamMaterials scienceScanning tunneling microscopeSiliconNanoscopic scaleRADIUSMicroscopyScanning probe microscopyField ion microscopeNanotechnologyGalliumIon milling machineOptoelectronicsOpticsIonChemistryPhysics

Abstract

fetched live from OpenAlex

Multi-probe scanning tunneling microscopy can play a role in various electrical measurements and characterization of nanoscale objects. The consistent close placement of multiple probes relies on very sharp apexes with no other interfering materials along the shank of the tip. Electrochemically etched tips can prepare very sharp apex tips; however, other asperities on the shank can cause interference and limit the close positioning of multiple tips to beyond the measured radii. Gallium focused ion beam (FIB) milling is used to remove any interfering material and allow closely spaced tips with a consistent yield. The tip apex radius is evaluated with field ion microscopy, and the probe spacing is evaluated with STM on hydrogen terminated silicon surfaces. FIB prepared tips can consistently achieve the measured probe to probe spacing distances of 25 nm–50 nm.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0020.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.043
GPT teacher head0.294
Teacher spread0.250 · 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 source (direct Gemma or distilled Codex), 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
Published2020
Admission routes2
Has abstractyes

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