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Optical excitation of atomic force microscopy cantilever for accurate spectroscopic measurements

2020· article· en· W3030899225 on OpenAlexafffund
Yoichi Miyahara, Harrisonn Griffin, Antoine Roy-Gobeil, Ron Belyansky, Hadallia Bergeron, José Bustamante, Peter Grütter

Bibliographic record

VenueEPJ Techniques and Instrumentation · 2020
Typearticle
Languageen
FieldPhysics and Astronomy
TopicForce Microscopy Techniques and Applications
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaMinistère de l'Éducation, du Loisir et du Sport Québec
KeywordsCantileverLaserOpticsExcitationNon-contact atomic force microscopyInterferometryMaterials scienceOptical forceResonance (particle physics)OptoelectronicsMicroscopyOptical tweezersPhysicsKelvin probe force microscopeAtomic physics

Abstract

fetched live from OpenAlex

Abstract Reliable operation of frequency modulation mode atomic force microscopy (FM-AFM) depends on a clean resonance of an AFM cantilever. It is recognized that the spurious mechanical resonances which originate from various mechanical components in the microscope body are excited by a piezoelectric elemen that is intended for exciting the AFM cantilever oscillation and these spurious resonance modes cause the serious undesirable signal artifacts in both frequency shift and dissipation signals. We present an experimental setup to excite only the oscillation of the AFM cantilever in a fiber-optic interferometer system using optical excitation force. While the optical excitation force is provided by a separate laser light source with a different wavelength (excitation laser : λ =1310 nm), the excitation laser light is still guided through the same single-mode optical fiber that guides the laser light (detection laser : λ =1550 nm) used for the interferometric detection of the cantilever deflection. We present the details of the instrumentation and its performance. This setup allows us to eliminate the problems associated with the spurious mechanical resonances such as the apparent dissipation signal and the inaccuracy in the resonance frequency measurement.

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 score0.122
Threshold uncertainty score0.574

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 head0.029
GPT teacher head0.323
Teacher spread0.294 · 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

Citations10
Published2020
Admission routes2
Has abstractyes

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