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Record W1972975780 · doi:10.1021/ac000335z

Double-Chained Surfactants for Semipermanent Wall Coatings in Capillary Electrophoresis

2000· article· en· W1972975780 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueAnalytical Chemistry · 2000
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Capillary Electrophoresis Applications
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsChemistryCapillary electrophoresisCapillary actionChromatographyElectrophoresisComposite material

Abstract

fetched live from OpenAlex

The double-chained cationic surfactant didodecyldimethylammonium bromide (DDAB) was found to form more stable coatings onto the walls of CE capillaries than similar single-chained surfactants such as cetyltrimethylammonium bromide (C16TAB). After removing DDAB from the buffer, the reversed EOF decreased only 3% over 75 min under continuous electrophoretic conditions. Also, the reversed EOF is 60% greater for DDAB than for C16TAB at pH 2. This greater coating stability is associated with a different aggregate structure for the surfactant at the capillary surface. The more homogeneous coating and greater surface coverage provided by DDAB allows the excess surfactant to be flushed from the capillary prior to performing electrophoretic separations. Separations of a basic protein mixture yielded quantitative recoveries, efficiencies ranging from 560,000 to 750,000 plates/m, and migration time reproducibility of 0.8-1.0% RSD (n = 10). This performance is similar to that of adsorbed cationic polymers (Polybrene, polyethyleneimine) but is achieved using a coating procedure that is over 10 times faster.

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.

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 categoriesInsufficient payload (model declined to judge)
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 score0.075
Threshold uncertainty score1.000

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)0.0010.000

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 head0.008
GPT teacher head0.217
Teacher spread0.209 · 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