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Record W1967085949 · doi:10.1021/ac035372f

Factors Affecting the Temporal Stability of Semipermanent Bilayer Coatings in Capillary Electrophoresis Prepared Using Double-Chained Surfactants

2004· article· en· W1967085949 on OpenAlexafffund
Mahmoud M. Yassine, Charles A. Lucy

Bibliographic record

VenueAnalytical Chemistry · 2004
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Capillary Electrophoresis Applications
Canadian institutionsAlberta Glycomics CentreUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsChemistryCoatingCapillary actionBuffer (optical fiber)Capillary electrophoresisBilayerCationic polymerizationChromatographyAdsorptionIonic strengthChemical engineeringReproducibilityAnalytical Chemistry (journal)Composite materialPolymer chemistryOrganic chemistryMembraneAqueous solutionMaterials science

Abstract

fetched live from OpenAlex

Surfactants such as didodecyldimethylammonium bromide (DDAB) adsorb onto fused-silica capillaries to form semipermanent bilayer coatings. However, such coatings must be regenerated between runs to maintain efficiency and reproducibility. In this paper, chemical and physical factors affecting the stability of DDAB coatings are investigated. Chemical factors such as ionic strength and the nature of the buffer anion (e.g., from acetate to phosphate), which decrease the critical micelle concentration of DDAB, improve the coating stability. Increasing buffer pH also increases the coating stability. Finally, reducing the capillary diameter and reducing the volume of buffer flushed through the capillary enhance the coating stability. Using 50 mM acetate, pH 5.0, in a 25-microm-i.d. capillary, cationic proteins were separated with efficiencies of 1.05 million plates/m and a run-to-run migration time reproducibility of 0.6-0.8% RSD for 10 successive runs without regeneration of the DDAB coating between runs.

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 score0.011
Threshold uncertainty score0.749

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 head0.025
GPT teacher head0.245
Teacher spread0.219 · 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

Citations74
Published2004
Admission routes2
Has abstractyes

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