MétaCan
Menu
Back to cohort
Record W1978775167 · doi:10.1002/elps.200500871

Joule heating effects on separation efficiency in capillary zone electrophoresis with an initial voltage ramp

2006· article· en· W1978775167 on OpenAlexaff
Xiangchun Xuan, Guoqing Hu, Dongqing Li

Bibliographic record

VenueElectrophoresis · 2006
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Capillary Electrophoresis Applications
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsJoule heatingVoltageMechanicsCapillary actionPressure gradientDispersion (optics)Electric fieldChemistryTemperature gradientViscosityJoule effectMaterials scienceFlow (mathematics)ThermalAnalytical Chemistry (journal)ThermodynamicsChromatographyPhysicsOpticsComposite materialMeteorology

Abstract

fetched live from OpenAlex

An analytical model is developed to quantify the Joule heating effects on the separation efficiency in CZE with an initial voltage ramp. This model considers the temporal variations of nonuniform temperature and flow fields in the course of voltage ramping. The temperature dependence of electrical conductivity, dynamic viscosity, and mass density of the fluid is also taken into account. We demonstrate that the application of an initial voltage ramp delays the development of pressure-driven flows induced passively by the axial temperature gradients. The thermal dispersion is thus significantly reduced, resulting in a higher theoretical plate number in CZE. Such improvement in the separation efficiency is apparent in noncoated capillaries at high electric fields with an appropriate voltage ramp-up time. These predictions are consistent with previous observations in both aqueous and nonaqueous CZE that took place in uncoated capillaries. In coated capillaries where the EOF is suppressed, however, our model predicts a lower plate number in the presence of an initial voltage ramp.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.003
GPT teacher head0.215
Teacher spread0.211 · 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

Citations22
Published2006
Admission routes1
Has abstractyes

Explore more

Same venueElectrophoresisSame topicMicrofluidic and Capillary Electrophoresis ApplicationsFrench-language works237,207