Effect of a Cationic Surfactant on Droplet Wetting on Superhydrophobic Surfaces
Bibliographic record
Abstract
We experimentally and theoretically examine the influence of a double-chain cationic surfactant, didodecyldimethylammonium bromide (DDAB), on the wetting states and contact angles on superhydrophobic (SH) surfaces made of hydrophobic microcylinders. We use two types of micropatterns of different surface roughness, r, and packing fraction, ϕ, and vary nine dimensionless surfactant concentrations ( C S ), normalized by the critical micelle concentration (CMC), in the experiments. At low C S, some of the surfactant-laden droplets are in a gas-trapping, Cassie–Baxter (CB) state on the high-roughness microstructures. In contrast, some droplets are in a complete-wetting Wenzel (W) state on the low-roughness microtextures. We found that the contact angle of CB drops can be well predicted using a thermodynamic model considering surfactant adsorption at the liquid–vapor (LV) and solid–liquid (SL) interfaces. At high C S, however, all of the DDAB drops wet in a Wenzel mode. Based on a Gibbsian thermodynamic analysis, we find that for the two types of superhydrophobic surfaces used, the Wenzel state has the lowest thermodynamic energy and thus is more favorable theoretically. The CB state, however, is metastable at low C S due to a thermodynamic energy barrier. The metastable CB wetting state becomes more stable on the SH microtextures with greater ϕ and r, in agreement with our experimental observations. Finally, we generalize this surface-energy analysis to provide useful designs of surface parameters for a DDAB-laden surfactant droplet on the SH surface with a stable and robust CB state.
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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 | 0.000 | 0.001 |
| 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) | 0.001 | 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".