MétaCan
Menu
Back to cohort
Record W116933999 · doi:10.1007/0-306-47095-0_8

A Force Limitation for Successful Observation of Atomic Defects: Defect Trappong of the Atomic Force Microscopy Tip

2005· book-chapter· en· W116933999 on OpenAlexaff
Igor Sokolov, Grant S. Henderson, F. J. Wicks

Bibliographic record

VenueKluwer Academic Publishers eBooks · 2005
Typebook-chapter
Languageen
FieldPhysics and Astronomy
TopicForce Microscopy Techniques and Applications
Canadian institutionsRoyal Ontario MuseumUniversity of Toronto
Fundersnot available
KeywordsConductive atomic force microscopyAtomic force microscopyKelvin probe force microscopeAtomic force acoustic microscopyCrystallographic defectNon-contact atomic force microscopyMaterials scienceElectrostatic force microscopeLattice (music)Molecular physicsAtomic physicsMagnetic force microscopeChemistryNanotechnologyPhysicsCrystallographyMagnetic field

Abstract

fetched live from OpenAlex

Theoretical simulations of atomic force microscopy (AFM) scans while operating in contact mode indicate that there is a natural limit to the maximum nondestructive scan force near atomic defects. This limit is much smaller than the force calculated for nondestructive scans on a defect free surface. The limit is a function of the nature of the sample lattice and imaging medium, and results from a specific force dependence between the AFM tip and sample near the defect, which essentially “traps” the AFM tip apex at constant height in the vicinity of the defect. The AFM feedback system is unable to respond to the trapping, and consequently, the monoatomic apex of the tip collides with the sample surface as the scan continues. The collision effectively produces either a multitip or removes the defect. Further, we find that for a lattice constant less than 0.29–0.3 nm, point-like atomic vacancies cannot be observed, regardless of the scan force used and the medium in which scans are performed.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.370
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.020
GPT teacher head0.273
Teacher spread0.253 · 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.

Study designTheoretical or conceptual
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
Published2005
Admission routes1
Has abstractyes

Explore more

Same venueKluwer Academic Publishers eBooksSame topicForce Microscopy Techniques and ApplicationsFrench-language works237,207