Semipermanent capillary coatings in mixed organic–water solvents for CE
Bibliographic record
Abstract
This report describes the creation of semipermanent capillary coatings that are compatible with organic-water solvent systems in CE. The coatings are created by simply rinsing the fused-silica capillary with long double-chain cationic surfactants, such as dimethyl-ditetradecyl ammonium bromide (2C(14)DAB), dihexadecyldimethyl ammonium bromide (2C(16)DAB), and dimethyldioctadecyl ammonium bromide (2C(18)DAB). These surfactants generate semipermanent bilayer coatings on the capillary surface, which display a high degree of stability in buffers containing up to 60% v/v of organic solvents, such as methanol and ACN. The coating stability increases with increasing hydrophobicity of the surfactant, i.e., with increasing chain length. For instance, the EOF changes by only 1.2% in a 2C(18)DAB-coated capillary after 130 capillary volumes of rinsing with 60% v/v methanol containing buffer. The bilayer coatings allow separations to be performed without the need to regenerate the coating between runs or to maintain the EOF modifier in the run buffer. Rapid separations (<2 min) of anions and basic drugs with migration time reproducibility of less than 0.5% RSD and efficiencies of 0.4-0.6 million plates/m are obtained. In addition, selectivity changes for small anions and cationic drugs are also observed when the organic solvent content is adjusted.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".