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Record W2011228448 · doi:10.1021/ac000393k

Dynamics of Capillary Isoelectric Focusing in the Absence of Fluid Flow:  High-Resolution Computer Simulation and Experimental Validation with Whole Column Optical Imaging

2000· article· en· W2011228448 on OpenAlexaff
Qinglu Mao, Janusz Pawliszyn, Wolfgang Thormann

Bibliographic record

VenueAnalytical Chemistry · 2000
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Capillary Electrophoresis Applications
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsChemistryIsoelectric focusingDetectorColumn (typography)Capillary actionAnodeAnalytical Chemistry (journal)Resolution (logic)Sample (material)Flow (mathematics)RADIUSChromatographyOpticsMechanicsElectrodeArtificial intelligencePhysics

Abstract

fetched live from OpenAlex

A 150-component, dynamic electrophoresis simulator was developed and applied to the description of capillary isoelectric focusing (CIEF) of amphoteric substances in quiescent solution. The simulator is shown to be capable of producing high-resolution pH 3-10 focusing data with 140 individual carrier ampholytes (20/pH unit) and at current densities that are used in CIEF, i.e., under conditions that were hitherto unaccessible by dynamic computer simulation. Having a focusing capillary of 5-cm length, the predicted focusing dynamics for amphoteric dyes obtained at a constant voltage of 1500 V (300 V/cm) are shown to qualitatively agree with data obtained by whole-column optical imaging. The simulation data provide detailed insight into the dynamics of the focusing process for the cases with the focusing column being sandwiched between 40 mM NaOH (catholyte) and 100 mM phosphoric acid (anolyte) or having the column ends only permeable for OH- and H+ at cathode and anode, respectively. Simulation data reveal that the number of sample boundaries migrating from the two ends of the column to the focusing positions is always equal to the number of sample components. The number of detectable migrating sample boundaries, however, can be lower. Whole-column optical imaging is demonstrated to be the method of choice for following the approach to equilibrium. With that detection format, transient sample peaks can be recognized and properly identified. This would also be possible with a scanning detector moving rapidly and repeatedly along the column but cannot be accomplished by a stationary detector placed at a specified location. The data presented demonstrate that the model together with imaging monitoring can be used to optimize the CIEF separation conditions.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.579
Threshold uncertainty score0.324

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 integrity0.0000.000
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 head0.004
GPT teacher head0.208
Teacher spread0.204 · 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 designSimulation or modeling
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

Citations60
Published2000
Admission routes1
Has abstractyes

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