MétaCan
Menu
Back to cohort
Record W2317589235 · doi:10.1021/ac200636y

Mass Transport in a Micro Flow-Through Vial of a Junction-at-the-Tip Capillary Electrophoresis-Mass Spectrometry Interface

2011· article· en· W2317589235 on OpenAlexaff
Xuefei Zhong, E. Jane Maxwell, David D. Y. Chen

Bibliographic record

VenueAnalytical Chemistry · 2011
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Capillary Electrophoresis Applications
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsChemistryCapillary electrophoresisAnalytical Chemistry (journal)AnalyteMass spectrometryCapillary actionVolumetric flow rateLaminar flowChromatographyCapillary electrophoresis–mass spectrometryElectrophoresisMechanicsElectrospray ionization

Abstract

fetched live from OpenAlex

When coupling capillary electrophoresis with postcolumn detection methods, such as mass spectrometry, the presence of postcolumn band broadening must be considered. The band broadening effects introduced by junction-at-the-tip CE-MS interfaces using a postcolumn micro flow-through vial are investigated by studying the hydrodynamic flow patterns and mass transport process inside the micro vial at the end of the CE separation capillary. Simulation results obtained by solving the Navier-Stokes and mass balance equations provide insights into the velocity field and concentration distribution of the analytes in the micro vial and demonstrate that, with a low flow rate of chemical modifier solution, the laminar flow streams confine the analyte molecules to the central part of the micro vial and thus maintain major features of the peak shapes. Peaks detected by UV and MS under similar experimental conditions were compared to verify the numerical prediction that the main features of the UV peak can be retained in the MS peak. Experiments also show that band broadening can be minimized when an appropriate chemical modifier flow rate is selected.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.047
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.009
GPT teacher head0.205
Teacher spread0.196 · 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.

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

Citations68
Published2011
Admission routes1
Has abstractyes

Explore more

Same venueAnalytical ChemistrySame topicMicrofluidic and Capillary Electrophoresis ApplicationsFrench-language works237,207