Electroactive Self-Assembled Monolayers Detect Micelle Formation
Bibliographic record
Abstract
The interfacial electrochemistry of self-assembled monolayers (SAMs) of ferrocenyldodecanethiolate on gold (FcC 12 SAu) electrodes is applied to detect the micellization of some common anionic surfactants, sodium n -alkyl sulfates, sodium n -alkyl sulfonates, sodium diamyl sulfosuccinate, and sodium dodecanoate, in aqueous solution by cyclic voltammetry. The apparent formal redox potential ( E °′ SAM ) of the FcC 12 SAu SAM is used to track changes in the concentration of the unaggregated surfactant anions and determine the critical micelle concentration (cmc). The effect of added salt (NaF) on the sodium alkyl sulfate concentration dependence of E °′ SAM is also investigated. Weakly hydrated anions, such as ClO 4 –, pair with the electrogenerated SAM-bound ferroceniums to neutralize the excess positive charge created at the SAM/electrolyte solution interface and stabilize the oxidized cations. E °′ SAM exhibits a Nernstian-type dependence on the anion activity in solution. Aggregation of the surfactant anions into micelles above the cmc causes the free surfactant anion activity to deviate from the molar concentration of added surfactant, resulting in a break in the plot of E °′ SAM versus the logarithm of the concentration of anionic surfactant. The concentration at which this deviation occurs is in good agreement with literature or experimentally determined values of the cmc. The effects of Ohmic potential drop, liquid junction potential, and surfactant adsorption behavior on E °′ SAM are addressed. Ultimately, the E °′ SAM response as a function of the anionic surfactant concentration exhibits the same features reported using potentiometry and surfactant ion-selective electrodes, which provide a direct measure of the free surfactant anion activity, thus making FcC 12 SAu SAM electrodes useful for the detection of surfactant aggregation and micelle formation.
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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 | 0.000 |
| 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".