Characterization of Surfactant Coatings in Capillary Electrophoresis by Atomic Force Microscopy
Bibliographic record
Abstract
This paper describes the adsorption mechanisms and aggregation properties of cetyltrimethylammonium bromide (CTAB) and didodecyldimethylammonium bromide (DDAB) surfactants that are used for dynamic coatings in capillary electrophoresis (CE). Atomic force microscopy is used to directly visualize surfactant adsorption on fused silica. It was found that the single-chained surfactant CTAB forms spherical aggregates on silica while the double-chained surfactant DDAB forms a bilayer. Aggregation at the surface occurs at approximately the same surfactant concentration in which EOF reversal is observed in CE. The nearest-neighbor distance between CTAB aggregates varies inversely with buffer pH and becomes constant at the point when the silanol groups are fully ionized. DDAB forms a flat, uniform coating independent of pH. Increasing the buffer ionic strength changes the morphology of the CTAB aggregates from spherical to cylindrical. The change in morphology can alter the surface coverage, which is related to the "normalized" EOF measured in identical buffers. The morphology of a surfactant coating is also shown to affect its ability to inhibit protein adsorption to the capillary wall. Specifically, the full surface coverage provided by DDAB proved superior in a head-to-head comparison with CTAB.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".