Colloid thermophoresis in surfactant solutions: Probing colloid–solvent interactions through microscale experiments
Bibliographic record
Abstract
Thermophoresis has emerged as a powerful tool for characterizing and manipulating colloids at the nano- and micro-scales due to its sensitivity to colloid-solvent interactions. The use of surfactants enables the tailoring of surface chemistry on colloidal particles and the tuning of interfacial interactions. However, the microscopic mechanisms underlying thermophoresis in surfactant solutions remain poorly understood due to the complexity of multiscale interaction coupling. To achieve a more fundamental understanding of the roles of surfactants, we investigated the thermophoretic behavior of silica beads in both ionic and nonionic surfactant solutions at various background temperatures. We provide a complete mechanistic picture of the effects of surfactants on interfacial interactions through mode-coupling analysis of both electrophoretic and thermophoretic experiments. Our results demonstrate that silica thermophoresis is predominantly governed by the dissociation of silanol functional groups at silica-water interfaces in nonionic surfactant solutions, while in ionic surfactant solutions, the primary mechanism driving silica thermophoresis is the adsorption of ionic surfactants onto the silica surface.
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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 | 0.000 |
| 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 | 0.001 |
| 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".