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Record W2063192182 · doi:10.1021/ac049229d

Coupling of Solid-Phase Microextraction and Capillary Isoelectric Focusing with Laser-Induced Fluorescence Whole Column Imaging Detection for Protein Analysis

2004· article· en· W2063192182 on OpenAlexafffund
Zhen Liu, Janusz Pawliszyn

Bibliographic record

VenueAnalytical Chemistry · 2004
Typearticle
Languageen
FieldChemistry
TopicMass Spectrometry Techniques and Applications
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsChemistryIsoelectric focusingChromatographySolid-phase microextractionDesorptionAnalytical Chemistry (journal)AnalyteCapillary actionFluorescencePhase (matter)Detection limitMass spectrometryAdsorptionMaterials science

Abstract

fetched live from OpenAlex

A coupling method of solid-phase microextraction (SPME) and capillary isoelectric focusing (CIEF) with laser-induced fluorescence (LIF) whole column imaging detection (WCID) was developed for the analysis of proteins. Unlike other liquid-phase separation methods and conventional CIEF, proteins are focused into stationary bands within a pH gradient in CIEF-WCID. Thus, CIEF-WCID is the most compatible liquid-phase separation method for coupling with SPME, which can effectively resolve the problems associated with the slow desorption kinetics of SPME in a liquid phase. By combining SPME and CIEF-WCID, the desorption time can be as long as necessary, allowing complete desorption without any band broadening and analyte carryover. By using this method, R-phycoerythrin in water can be extracted by SPME in 10 min, and subsequently analyzed by CIEF-LIF-WCID within 20 min, providing a limit of detection of 3.5 x 10(-12) M (S/N = 3). The feasibility of the SPME-CIEF-LIF-WCID method was demonstrated by extracting and analyzing extracellular phycoerythrins in cultured cyanobacteria samples. Extracellular phycoerythrins at the nanomolar level were extracted and analyzed in 30 min, while avoiding the interference of the cyanobacteria cells.

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 score0.096
Threshold uncertainty score0.746

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)0.0000.000

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 head0.010
GPT teacher head0.290
Teacher spread0.280 · 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

Citations39
Published2004
Admission routes2
Has abstractyes

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