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Record W2009560544 · doi:10.1021/ac801094s

Dielectrophoretic and Electrothermal Effects at Alternating Current Heated Disk Microelectrodes

2008· article· en· W2009560544 on OpenAlexaff
Aliaksei Boika, Andrzej Barański

Bibliographic record

VenueAnalytical Chemistry · 2008
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Bio-sensing Technologies
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsMicroelectrodeElectrodeChemistryAlternating currentElectric fieldPolarization (electrochemistry)Analytical Chemistry (journal)ElectrohydrodynamicsOptoelectronicsVoltageMaterials scienceElectrical engineering

Abstract

fetched live from OpenAlex

When a disk microelectrode is polarized with an alternating potential of very high frequency (0.1-2 GHz) and a high amplitude (up to 2.8 V rms), the electrode is heated up, and at the same time, a very intense electric field is created around the electrode (>10(6) V/m for electrodes 1 microm in radius). This strong electric field gives rise to positive or negative dielectrophoretic effects. Positive dielectrophoretic effects can be used to assemble nanowires from nanoparticles at the electrode edge. On the other hand, a negative dielectrophoretic effect is probably responsible for "jet boiling" observed at overheated microelectrodes. In addition, a combination of a high temperature gradient and a high potential gradient generates an intense electrothermal flow of solution which very strongly enhances the mass transport and is responsible for intense convection in such systems. The electrothermal flow and dielectrophoretic forces can be generated directly on a microelectrode employed in electrochemical detection because the high frequency ac polarization of the electrode does not interfere with the acquisition of analytical signals.

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 score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.006
GPT teacher head0.188
Teacher spread0.183 · 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

Citations41
Published2008
Admission routes1
Has abstractyes

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