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Record W2044928750 · doi:10.1063/1.4903484

Accurate formula for dissipative interaction in frequency modulation atomic force microscopy

2014· article· en· W2044928750 on OpenAlexaff
Kazuhiro Suzuki, Kei Kobayashi, Aleksander Labuda, Kazumi Matsushige, Hirofumi Yamada

Bibliographic record

VenueApplied Physics Letters · 2014
Typearticle
Languageen
FieldPhysics and Astronomy
TopicForce Microscopy Techniques and Applications
Canadian institutionsMcGill University
Fundersnot available
KeywordsCantileverDissipationDissipative systemMicaNanometreMaterials scienceNon-contact atomic force microscopySurface forces apparatusFrequency modulationConductive atomic force microscopyOpticsAtomic force acoustic microscopyMechanicsAtomic force microscopyNanotechnologyPhysicsThermodynamicsMagnetic force microscopeComposite material

Abstract

fetched live from OpenAlex

Much interest has recently focused on the viscosity of nano-confined liquids. Frequency modulation atomic force microscopy (FM-AFM) is a powerful technique that can detect variations in the conservative and dissipative forces between a nanometer-scale tip and a sample surface. We now present an accurate formula to convert the dissipation power of the cantilever measured during the experiment to damping of the tip-sample system. We demonstrated the conversion of the dissipation power versus tip-sample separation curve measured using a colloidal probe cantilever on a mica surface in water to the damping curve, which showed a good agreement with the theoretical curve. Moreover, we obtained the damping curve from the dissipation power curve measured on the hydration layers on the mica surface using a nanometer-scale tip, demonstrating that the formula allows us to quantitatively measure the viscosity of a nano-confined liquid using FM-AFM.

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)0.002
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.003
Open science0.0020.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.001

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 head0.011
GPT teacher head0.288
Teacher spread0.277 · 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 designSimulation or modeling
Domainnot available
GenreMethods

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

Citations6
Published2014
Admission routes1
Has abstractyes

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