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Record W2087442402 · doi:10.1021/ac202130f

Flow-Through Microvial Facilitating Interface of Capillary Isoelectric Focusing and Electrospray Ionization Mass Spectrometry

2011· article· en· W2087442402 on OpenAlexafffund
Xuefei Zhong, E. Jane Maxwell, Chitra Ratnayake, Scott Mack, David D. Y. Chen

Bibliographic record

VenueAnalytical Chemistry · 2011
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Capillary Electrophoresis Applications
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsChemistryIsoelectric focusingElectrospray ionizationMass spectrometryCapillary electrophoresisCapillary electrophoresis–mass spectrometryElectrosprayChromatographyAnalytical Chemistry (journal)Capillary actionIonizationExtractive electrospray ionizationElectrophoresisCapillary electrochromatographyAnalyteIonSample preparation in mass spectrometry

Abstract

fetched live from OpenAlex

A capillary electrophoresis mass spectrometry (CE-MS) interface utilizing a flow-through microvial is used to ensure the electric continuity and supply the catholyte and mobilizer solutions during the capillary isoelectric focusing (cIEF) and mobilization process. The flow-through microvial provides a stable chemical environment and helps to improve the ionization efficiency without significantly diluting the analyte. The CE-MS interface facilitates the transfer of the mobilized cIEF effluent to the site of electrospray ionization, and the gaseous ions can be detected directly by a mass spectrometer. It also allows for complete focusing and mobilization processes to be performed automatically in programmed sequences with commercial CE systems. Two different strategies, using either a part of the capillary or the flow-through microvial of the CE-MS interface as the catholyte reservoir for bare fused silica capillaries or neutral coated capillaries, respectively, were developed for automated cIEF-electrospray ionization (ESI)-MS. Reasonable separation efficiency was achieved using proper concentration of carrier ampholytes and suitable strategies of electroosmotic/electrophoretic mobilization.

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: Methods · Consensus signal: Methods
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.216
Teacher spread0.207 · 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
GenreMethods

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

Citations66
Published2011
Admission routes2
Has abstractyes

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