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<i>In Situ</i> Reproducible Sharp Tips for Atomic Force Microscopy

2021· article· en· W3147891320 on OpenAlexaff
Jo Onoda, Tsuyoshi Hasegawa, Yoshiaki Sugimoto

Bibliographic record

VenuePhysical Review Applied · 2021
Typearticle
Languageen
FieldPhysics and Astronomy
TopicForce Microscopy Techniques and Applications
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsConductive atomic force microscopyScanning tunneling microscopeAtomic force microscopyMaterials scienceNon-contact atomic force microscopyPhotoconductive atomic force microscopyCantileverIonic bondingMicroscopyResolution (logic)Scanning capacitance microscopyNanotechnologyCrystallographyPhysicsOpticsChemistryIonScanning confocal electron microscopy

Abstract

fetched live from OpenAlex

Atomically sharp tips are a requirement for scanning-probe microscopy, such as scanning tunneling microscopy (STM) and atomic force microscopy (AFM). Compared with STM, AFM imaging is more sensitive to the sharpness of tip apices because long-range forces act as a background signal on the high-resolution AFM images originating from short-range forces. Here we report the investigation of in situ reproducible sharp tips for AFM. We make an ${\mathrm{Ag}}_{2}\mathrm{S}$ crystal, a mixed ionic and electronic conductor, on a conventional $\mathrm{Si}$ cantilever, and controllably grow and shrink the $\mathrm{Ag}$ nanoprotrusion by changing the polarity of the bias voltage between the tip and the sample. We are able to reduce the contribution of long-range forces by growing a $\mathrm{Ag}$ nanoprotrusion on the ${\mathrm{Ag}}_{2}\mathrm{S}$ tip, and obtain atomic-resolution AFM images. We also confirm that the ${\mathrm{Ag}}_{2}\mathrm{S}$ tip with a $\mathrm{Ag}$ nanoprotrusion, the end of which presumably terminates in $\mathrm{Si}$ atoms, is capable of simultaneous AFM and STM measurements.

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 score0.581
Threshold uncertainty score0.830

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 head0.014
GPT teacher head0.341
Teacher spread0.327 · 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
Published2021
Admission routes1
Has abstractyes

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