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Record W2152756410 · doi:10.1088/0957-0233/20/2/025703

Frequency-dependent viscoelasticity measurement by atomic force microscopy

2008· article· en· W2152756410 on OpenAlexafffund
Nan Yang, Kenneth Kar Ho Wong, John R. de Bruyn, Jeffrey L. Hutter

Bibliographic record

VenueMeasurement Science and Technology · 2008
Typearticle
Languageen
FieldPhysics and Astronomy
TopicForce Microscopy Techniques and Applications
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsViscoelasticityMaterials scienceSelf-healing hydrogelsElastic modulusVinyl alcoholComposite materialModulusDynamic modulusAtomic force acoustic microscopyDynamic mechanical analysisDeflection (physics)Atomic force microscopyNanomechanicsElasticity (physics)AmplitudeMicroscopyYoung's modulusOpticsNanotechnologyPolymer chemistryMagnetic force microscopePhysicsPolymer

Abstract

fetched live from OpenAlex

We demonstrate a new technique for investigating viscoelastic properties of soft materials using the atomic force microscope. A small oscillatory voltage is added to the deflection signal of the atomic force microscope causing a vertical oscillatory sample motion. Monitoring the amplitude and phase of this motion allows determination of the viscous and elastic moduli of the sample as a function of frequency during contact imaging. This technique is applied to suspended poly(vinyl alcohol) nanofibers and poly(vinyl alcohol) hydrogels, giving results similar to those measured using traditional static methods. However, the moduli of both the fibers and the hydrogels show a significant frequency dependence. The Young's modulus of the fibers increases with frequency, while for the viscoelastic hydrogels, the storage modulus dominates the mechanical response at low frequency whereas the loss modulus dominates at high frequency.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.018
GPT teacher head0.254
Teacher spread0.236 · 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 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

Citations29
Published2008
Admission routes2
Has abstractyes

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