Dielectric relaxation spectroscopy of aqueous micellar electrolyte solutions: A novel application to infer Dukhin number and zeta potential of a micelle
Bibliographic record
Abstract
The complex permittivities of aqueous SDS solutions, with and without the addition of sodium chloride (NaCl), are measured in the frequency range from 200 MHz to 14 GHz. The SDS concentrations are chosen such that the SDS molecules aggregate to micelles. In this frequency range, the measured spectra allow for the identification of two different relaxation processes. That is, the relaxation of the water molecules at frequencies above 1 GHz and the micellar relaxation at frequencies lower than 1 GHz. It is found that the addition of NaCl to the system mostly affects the micellar relaxation process. In detail, the time constant as well as the amplitude of the relaxation decrease by adding NaCl. These effects are attributed to the change in the solution conductivity that changes the properties of the micelle's electrical double layer. We also extract the Dukhin number of the micelles as a function of surfactant and electrolyte content from the measurements. The Dukhin number is a dimensionless group that describes the influence of the surface conductivity on a phenomena. A regression between Dukhin numbers and free sodium ions is found so that all data collapses on a single curve independent of the surfactant concentration. The surface conductivity is a manifestation of the electrical double layer and we use the Bikerman equation to infer the zeta potential of the micelles. Comparison to literature data shows very good agreement and proves that dielectric relaxation spectroscopy can be engaged to infer the zeta potential of micelles. Abbreviations: CMC critical micelle concentration, DRS dielectric relaxation spectroscopy, EDL electrical double layer.
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.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".