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Record W2031349180 · doi:10.1063/1.4872383

Four-probe measurements with a three-probe scanning tunneling microscope

2014· article· en· W2031349180 on OpenAlexaff
Mark Salomons, Bruno Martins, Janik Zikovsky, Robert A. Wolkow

Bibliographic record

VenueReview of Scientific Instruments · 2014
Typearticle
Languageen
FieldPhysics and Astronomy
TopicSurface and Thin Film Phenomena
Canadian institutionsUniversity of AlbertaNational Institute for Nanotechnology
Fundersnot available
KeywordsMaterials scienceScanning probe microscopyScanning tunneling microscopeScanning Hall probe microscopeMicroscopeConductive atomic force microscopyScanning electron microscopeOpticsScanning ion-conductance microscopyField ion microscopeElectrochemical scanning tunneling microscopeConventional transmission electron microscopeScanning capacitance microscopyOptoelectronicsNanotechnologyScanning confocal electron microscopyScanning tunneling spectroscopyAtomic force microscopyScanning transmission electron microscopyIonPhysics

Abstract

fetched live from OpenAlex

We present an ultrahigh vacuum (UHV) three-probe scanning tunneling microscope in which each probe is capable of atomic resolution. A UHV JEOL scanning electron microscope aids in the placement of the probes on the sample. The machine also has a field ion microscope to clean, atomically image, and shape the probe tips. The machine uses bare conductive samples and tips with a homebuilt set of pliers for heating and loading. Automated feedback controlled tip-surface contacts allow for electrical stability and reproducibility while also greatly reducing tip and surface damage due to contact formation. The ability to register inter-tip position by imaging of a single surface feature by multiple tips is demonstrated. Four-probe material characterization is achieved by deploying two tips as fixed current probes and the third tip as a movable voltage probe.

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)0.002
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.499
Threshold uncertainty score0.782

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.037
GPT teacher head0.259
Teacher spread0.222 · 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 designOther design
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

Citations8
Published2014
Admission routes1
Has abstractyes

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