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Molecular dynamics simulation of amplitude modulation atomic force microscopy

2015· article· en· W1953044916 on OpenAlexafffund
Xiao‐Li Hu, Philip Egberts, Yalin Dong, Ashlie Martini

Bibliographic record

VenueNanotechnology · 2015
Typearticle
Languageen
FieldPhysics and Astronomy
TopicForce Microscopy Techniques and Applications
Canadian institutionsUniversity of Calgary
FundersUniversity of AkronUniversity of CalgaryNational Science Foundation
KeywordsAmplitudeMolecular dynamicsMaterials scienceOscillation (cell signaling)DissipationSubstrate (aquarium)Atomic force microscopyModulation (music)Amplitude modulationMolecular physicsExcitationAtomic unitsNon-contact atomic force microscopyFrequency modulationDynamics (music)SIGNAL (programming language)Conductive atomic force microscopyNanotechnologyOpticsPhysicsChemistryAcousticsRadio frequencyThermodynamicsQuantum mechanics

Abstract

fetched live from OpenAlex

Molecular dynamics (MD) simulations were used to model amplitude modulation atomic force microscopy (AM-AFM). In this novel simulation, the model AFM tip responds to both tip-substrate interactions and to a sinusoidal excitation signal. The amplitude and phase shift of the tip oscillation observed in the simulation and their variation with tip-sample distance were found to be consistent with previously reported trends from experiments and theory. These simulation results were also fit to an expression enabling estimation of the energy dissipation, which was found to be smaller than that in a corresponding experiment. The difference was analyzed in terms of the effects of tip size and substrate thickness. Development of this model is the first step toward using MD to gain insight into the atomic-scale phenomena that occur during an AM-AFM 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 score0.467
Threshold uncertainty score0.509

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 head0.010
GPT teacher head0.297
Teacher spread0.287 · 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

Citations19
Published2015
Admission routes2
Has abstractyes

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