Determination of Critical Micelle Concentration of Ionic and Non‐Ionic Surfactants by Streaming Potential Measurements
Bibliographic record
Abstract
ABSTRACT A capillary electrophoresis system capable of measuring streaming potentials was used for the determination of critical micelle concentration (CMC) of anionic, cationic, zwitterionic and non‐ionic surfactants. The CMC values of anionic surfactant sodium dodecyl sulphate (SDS), cationic surfactant cetyltrimethylammonium bromide (CTAB), zwitterionic surfactant 3‐((3‐cholamidopropyl) dimethylammonio)‐1‐propanesulfonate (CHAPS) and non‐ionic surfactant polyethylene glycol p ‐(1,1,3,3‐tetramethylbutyl)‐phenyl ether (Triton X‐100) in water or salt solutions were determined by determining the abrupt change in the trend of streaming potential change with the surfactant concentration. The CMC values were 8.23, 0.93, 5.80 and 0.16 mM, respectively. This method was also used to demonstrate how the CMCs of SDS and CTAB change differently with temperature. The CMC of SDS decreased from 10°C to 25°C and then increased from 25°C to 40°C, whereas CTAB only increased linearly within 10°C–40°C. The capillary wall zeta potentials in surfactant solutions can be calculated from the measured streaming potential, conductivity and solution viscosity. Surface charge densities were calculated using the zeta potentials obtained. The surface charge densities of SDS were calculated to be 5.6–0.8 C/m 2 when SDS solutions with concentrations of 2–20 mM zeta potentials were used. The calculated zeta potentials and surface charge densities reached a plateau at about 8 mM, which coincided with the CMC of SDS determined in the present study and the literature values. The CMC values obtained from streaming potential measurement are comparable to values obtained with other CMC determination techniques such as surface tension and conductometric measurements.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".