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Record W2981415801 · doi:10.1063/1.5112062

Force spectroscopy using a quartz length-extension resonator

2019· article· en· W2981415801 on OpenAlexaff
Yoshiaki Sugimoto, Jo Onoda

Bibliographic record

VenueApplied Physics Letters · 2019
Typearticle
Languageen
FieldPhysics and Astronomy
TopicForce Microscopy Techniques and Applications
Canadian institutionsUniversity of Alberta
FundersJapan Society for the Promotion of ScienceToray Science FoundationAsahi Glass Foundation
KeywordsStiffnessForce spectroscopyNon-contact atomic force microscopyAmplitudeResonatorOscillation (cell signaling)CantileverElasticity (physics)Atomic force acoustic microscopyVibrationElectrostatic force microscopeMagnetic force microscopeMaterials scienceMolecular physicsPhysicsChemistryOpticsMagnetic fieldAcousticsAtomic force microscopyConductive atomic force microscopyNanotechnologyComposite material

Abstract

fetched live from OpenAlex

Frequency modulation atomic force microscopy detects the interaction force between the tip and the sample by measuring the change in the resonance frequency of an oscillating force sensor. Short-range interaction force can be selectively detected by a small oscillation amplitude. A quartz length-extension resonator (LER) offers the advantage of small-amplitude operation by virtue of its ultrahigh stiffness. Here, we demonstrate that an LER can accurately measure the short-range interaction force at cryogenic temperature even under a high magnetic field. We derive a formula for calculating the effective stiffness of an oscillating LER by using the theory of elasticity. The obtained dynamic stiffness is 1.23 times greater than the static stiffness, and this difference significantly affects the estimation of the interaction force. Using a properly calibrated LER, force curves are measured on Si(111)-(7 × 7) surfaces. The maximum attractive short-range forces above Si adatoms using several tip apex states are in the ranges of the values previously obtained by Si cantilevers.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.107
Threshold uncertainty score1.000

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 head0.009
GPT teacher head0.254
Teacher spread0.244 · 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 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

Citations16
Published2019
Admission routes1
Has abstractyes

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