Dynamic capillary coatings with zwitterionic surfactants for capillary isoelectric focusing
Bibliographic record
Abstract
The use of surfactants as additives was demonstrated for the first time in capillary isoelectric focusing (CIEF) to dynamically modify the surfaces of bare fused silica capillaries. These surfactants were zwitterionic sulfobetaines: dodecyldimethyl (3-sulfopropyl) ammonium hydroxide (C12N3SO3), hexadecyldimethyl (3-sulfopropyl) ammonium hydroxide (C16N3SO3) and coco (amidopropyl)hydroxyldimethylsulfobetaine (Rewoteric AM CAS U). They were added directly to the protein-ampholyte mixture, and remained in the capillary during isoelectric focusing and mobilization. The C16N3SO3 and CAS U coatings were shown effective in CEF. Separation of seven IEF protein standards was obtained, with significantly improved resolution compared to that from an uncoated silica capillary. The effect of these surfactants on the electroosmotic flow (EOF) in CIEF was determined. CAS U was effective in suppressing the EOF at neutral and alkaline pH conditions, C16N3SO3 was effective in suppressing EOF at acidic and neutral pH conditions. C12N3SO3 however had little effect on the EOF. The pH gradients formed inside these surfactant coated capillaries were recta-linear at pH 6 to 9 (R2 approximately equal to 0.99). Reproducibility of migration time and peak area was determined. For all three coatings, the migration time standard deviations were less than 1.6 min, and the relative standard deviations of area were below 10%. The protein recovery in the CAS U-modified capillary was quantitative or near-quantitative for five of the seven proteins studied.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 0.000 |
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.
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".