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Record W2905417424 · doi:10.1002/elps.201800376

Counterflow gradient electrophoresis for focusing and elution

2018· review· en· W2905417424 on OpenAlexafffund
Matthew Courtney, Carolyn L. Ren

Bibliographic record

VenueElectrophoresis · 2018
Typereview
Languageen
FieldEngineering
TopicMicrofluidic and Capillary Electrophoresis Applications
Canadian institutionsUniversity of Waterloo
FundersWaterloo Institute for Nanotechnology, University of WaterlooNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsUniversity of WaterlooCanada Foundation for Innovation
KeywordsAnalyteIsoelectric focusingElectrophoresisIsoelectric pointChromatographyElutionAnalytical Chemistry (journal)Resolution (logic)ChemistryGradient elutionMaterials scienceHigh-performance liquid chromatographyComputer science

Abstract

fetched live from OpenAlex

Counterflow gradient electrofocusing uses the bulk flow of a liquid solution to counterbalance the electrophoretic migration of an analyte. When either the bulk velocity or the electrophoretic velocity of the analyte is made to vary across the length of the channel, there exists a unique zero-velocity point for the analyte. This focusing method enables simultaneous separation and concentration of different analytes. The high resolution and sensitivity achieved are similar to that of isoelectric focusing, which separates analytes based on their isoelectric points, but the key difference is that analytes will instead focus based on their electrophoretic mobility. Dynamically changing the applied voltage or the counterflow rate over time will shift the zero-velocity point, and therefore allows the focused analytes to pass through a fixed detection point, or elute from the separation channel. Throughout the review, a number of different counterflow gradient techniques will be discussed, along with their recent advancements and potential applications.

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)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.542
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.017
GPT teacher head0.256
Teacher spread0.240 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations6
Published2018
Admission routes2
Has abstractyes

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