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Record W2322126741 · doi:10.1021/ac101980k

Temperature Difference between the Cooled and the Noncooled Parts of an Electrolyte in Capillary Electrophoresis

2010· article· en· W2322126741 on OpenAlexafffund
Michael U. Musheev, Yuri Filiptsev, Sergey N. Krylov

Bibliographic record

VenueAnalytical Chemistry · 2010
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Capillary Electrophoresis Applications
Canadian institutionsYork University
FundersNatural Sciences and Engineering Research Council of CanadaYork University
KeywordsCapillary electrophoresisChemistryCapillary actionJoule heatingElectrolyteInletAnalytical Chemistry (journal)ChromatographyThermal conductionElectrophoresisJoule–Thomson effectConductivityThermodynamicsElectrodeComposite materialMaterials science

Abstract

fetched live from OpenAlex

Joule heating always accompanies electrophoresis and unavoidably leads to a temperature increase of the electrolyte. The elevated temperatures are known to adversely affect the quality of separation and detection. To minimize the temperature increase in capillary electrophoresis (CE), Joule heat is removed by actively cooling the capillary. However, there are always small parts of the capillary, such as its inlet, outlet, and detection window, which are not actively cooled. The noncooled capillary inlet has been recently proven to have an elevated temperature which is high enough to significantly affect CE-based quantitative affinity analyses. The temperature difference between the cooled and noncooled regions has never been determined due to the lack of a suitable method. Here, we report on the first experimental determination of temperature in the cooled part of the capillary and the noncooled inlet region of the capillary. We found that, under typical CE conditions, with a low-conductivity run buffer, the temperature in the noncooled inlet exceeded the temperature in the cooled region by more than 15 °C. High-conductivity buffers are anticipated to have even greater temperature differences between the noncooled and cooled capillary parts. Our results strongly suggest the potential effect of the noncooled capillary regions on the quality of CE-based analyses, which cannot be ignored. The simplest way to avoid potential errors is to move the sample to the cooled region by pressure or by applying a low electric field.

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 score0.024
Threshold uncertainty score0.418

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 integrity0.0000.001
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 head0.195
Teacher spread0.192 · 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

Citations32
Published2010
Admission routes2
Has abstractyes

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