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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 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)0.001
metaresearch head score (Gemma)0.002
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0050.000

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 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
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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