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Record W2050553628 · doi:10.1002/elps.201100027

A promising capillary electrophoresis–electrospray ionization–mass spectrometry method for carbohydrate analysis

2011· article· en· W2050553628 on OpenAlexaff
E. Jane Maxwell, Chitra Ratnayake, Roxana Jayo, Xuefei Zhong, David D. Y. Chen

Bibliographic record

VenueElectrophoresis · 2011
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Capillary Electrophoresis Applications
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsCapillary actionElectrospray ionizationElectrosprayChemistryChromatographyCapillary electrophoresisAnalytical Chemistry (journal)Mass spectrometryVolumetric flow rateAnalyteCapillary electrophoresis–mass spectrometryCapillary electrochromatographyDilutionResolution (logic)Materials science

Abstract

fetched live from OpenAlex

A method for adapting widely used CE conditions for the separation of fluorescently labeled carbohydrates to permit online ESI-MS detection is presented. Reverse polarity separations were performed in bare fused-silica capillaries with an acidic BGE. Under these conditions, negatively charged 8-aminopyrene 1,3,6-trisulfonate-labeled carbohydrates migrate forward against the EOF, which is towards the capillary inlet. Therefore, the CE-MS interface must simultaneously back-fill the capillary, in order to maintain the CE circuit, and provide a stable forward flow at the sprayer tip to support the electrospray process. This was achieved using a junction-at-the-tip interface, which provides a flow of solution to the junction formed by the capillary terminus and the inner wall of the emitter needle tip. Because the flow rate required for this arrangement is much less than in conventional sheath flow interfaces, dilution of the analytes is minimized. Optimized separation conditions permit baseline resolution of glucose oligomers containing up to 15 glucose units, while longer oligomers, up to 33 glucose units, were observed as resolved peaks in the negative ion mode mass spectrum.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.764
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.006
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.010
GPT teacher head0.221
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 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

Citations34
Published2011
Admission routes1
Has abstractyes

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