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Record W2331579841 · doi:10.1021/la303940a

Influence of Electrostatic Forces on Particle Propulsion in the Evanescent Field of Silver Ion-Exchanged Waveguides

2013· article· en· W2331579841 on OpenAlexafffund
Dmytro Gebennikov, Silvia Mittler

Bibliographic record

VenueLangmuir · 2013
Typearticle
Languageen
FieldPhysics and Astronomy
TopicOrbital Angular Momentum in Optics
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of CanadaGovernment of CanadaGovernment of OntarioOntario Innovation Trust
KeywordsIonic strengthIonElectrostaticsChemistryIonic bondingParticle (ecology)RADIUSCharged particleDipoleAqueous solutionWaveguideDielectricPropulsionChemical physicsMolecular physicsMaterials scienceOpticsPhysicsThermodynamicsOptoelectronicsPhysical chemistry

Abstract

fetched live from OpenAlex

The effect of electrostatic interaction between carboxylate- and amino-functionalized polystyrene particles and a charged waveguide surface on the propulsion speed in optical tweezers is considered to be a function of the pH and ionic strength. It was shown that with the variation of the pH of the aqueous solution in which the particles were immersed, a systematic change in propulsion speed with a maximum speed could be achieved. The appearance of a maximum speed was ascribed to changes in the particle-waveguide separation as a result of the combination of two forces: Coulomb repulsion/attraction and induced dipole forces. The highest maximum speed at low ionic strength was around 12 μm/s. Changes in the ionic strength of the solution influenced the gradient of the dielectric constant near the involved surfaces and also led to a slightly reduced hydrodynamic radius of the particles. The combination of these effects subsequently increased the maximum speed to about 23 μm/s.

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 score0.273
Threshold uncertainty score0.158

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 head0.006
GPT teacher head0.241
Teacher spread0.235 · 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

Citations1
Published2013
Admission routes2
Has abstractyes

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