Colloid thermophoresis in the dilute electrolyte concentration regime: from theory to experiment
Bibliographic record
Abstract
Colloid thermophoresis in aqueous media is vital for numerous applications in nanoscience and life sciences. To date, a general description of colloid thermophoresis in DI water has not been determined. Here, we describe a theoretical model within the framework of the Fokker-Planck formalism and the flickering cluster concept to describe the hydration entropy effect on the thermophoretic behaviour of colloids suspended in DI water and compare this to new experimental results. We built an experimental platform to allow for rapid and robust temperature control and investigate the thermophoretic behaviour of silica microspheres with different sizes at various background temperatures for comparison. In this work, the ionic shielding effect is accounted for by using the well-known Duhr-Dhont's model, and the hydration layer effect is determined using the developed theoretical model. For the latter, our model reveals that the sign of the Soret coefficient is governed by the interplay between the binding energy and the chemical potential of water molecules, which were found to be in the same order of magnitude. We show that our analysis accurately describes the experimental behaviour of colloidal particles that opens a new avenue for developing versatile trapping and separation techniques for various colloidal particles in aqueous systems according to their size and background temperature.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".