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Record W2076342446 · doi:10.1103/physrevlett.92.146103

Sublattice Identification in Scanning Force Microscopy on Alkali Halide Surfaces

2004· article· en· W2076342446 on OpenAlexfundno aff
R. Hoffmann, Lev Kantorovich, A. Baratoff, Hans J. Hug, H.‐J. Güntherodt

Bibliographic record

VenuePhysical Review Letters · 2004
Typearticle
Languageen
FieldPhysics and Astronomy
TopicForce Microscopy Techniques and Applications
Canadian institutionsnot available
FundersMcGill UniversityAlexander von Humboldt-StiftungSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungNational Science Foundation
KeywordsScanning Force MicroscopyMaterials scienceCleavage (geology)HalideIonic bondingCrystal (programming language)Alkali metalChemical physicsSymmetry (geometry)Range (aeronautics)MicroscopyMolecular physicsPolarity (international relations)Atomic force microscopyIonCrystallographyOpticsChemistryGeometryNanotechnologyPhysicsInorganic chemistryComposite material

Abstract

fetched live from OpenAlex

We propose and apply to the KBr(001) surface a new procedure for species recognition in scanning force microscopy (SFM) of ionic crystal surfaces which show a high symmetry of the charge arrangement. The method is based on a comparison between atomistic simulations and site-specific frequency versus distance measurements. First, by taking the difference of force-distance curves extracted at a few judiciously chosen surface sites we eliminate site-independent long-range forces. The obtained short-range force differences are then compared with calculated ones assuming plausible tip apex models. This procedure allows for the first time identification of the tip apex polarity and of the positive and negative sublattices in SFM images of the (001) cleavage surface of an ionic crystal with the rock salt structure.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.013
GPT teacher head0.332
Teacher spread0.319 · 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

Citations108
Published2004
Admission routes1
Has abstractyes

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