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Record W2001639179 · doi:10.1021/ac048909d

Enhanced Stability Self-Assembled Coatings for Protein Separations by Capillary Zone Electrophoresis through the Use of Long-Chained Surfactants

2004· article· en· W2001639179 on OpenAlexafffund
Mahmoud M. Yassine, Charles A. Lucy

Bibliographic record

VenueAnalytical Chemistry · 2004
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Capillary Electrophoresis Applications
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaBristol-Myers Squibb
KeywordsChemistryPulmonary surfactantBromideCationic polymerizationCoatingCapillary actionIodideChromatographyCapillary electrophoresisChemical engineeringPolymer chemistryOrganic chemistryComposite material

Abstract

fetched live from OpenAlex

Semipermanent coatings were generated within fused-silica capillaries by flushing the capillary with a 0.1 mM solution of the double-chained cationic surfactants didodecyldimethylammonium bromide, dimethylditetradecylammonium bromide (2C(14)DAB), dihexadecyldimethylammonium bromide, and dimethyldioctadecylammonium bromide (2C(18)DAB) and the triple-chained surfactant tridodecylmethylammonium iodide. All of these coatings were semipermanent, whereby the coating remained intact after the unadsorbed surfactant was removed from the capillary. The separation efficiencies for four model cationic proteins ranged from 1.2 to 1.4 million plates/m for the 2C(14)DAB coating to 0.3-0.4 million plates/m for the 2C(18)DAB coatings. The stability of the coating increased with increasing hydrophobicity of the surfactant (i.e., increasing chain length and decreasing cmc). Over 60 successive separations were performed on a 2C(18)DAB-coated capillary over 12 days, without any regeneration of the coating. The migration times varied by less than 2.3% over this period with no loss in efficiency.

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 score0.095
Threshold uncertainty score0.789

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 head0.016
GPT teacher head0.233
Teacher spread0.217 · 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

Citations57
Published2004
Admission routes2
Has abstractyes

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