AC Electrokinetic Templating of Colloidal Particle Assemblies: Effect of Electrohydrodynamic Flows
Bibliographic record
Abstract
The use of spatially nonuniform electric fields for the contact-free colloidal particle assembly into ordered structures of various length scales is a research area of great interest. In the present work, numerical simulations are undertaken in order to advance our understanding of the physical mechanisms that govern this colloidal assembly process and their relation to the electric field characteristics and colloidal system properties. More specifically, the electric-field driven assembly of colloidal silica (d(p) = 0.32 and 2 μm) in DMSO, a near index matching fluid, is studied numerically over a range of voltages and concentration by means of a continuum thermodynamic approach. The equilibrium (u(f) = 0) and nonequilibrium (u(f) ≠ 0) cases were compared to determine whether fluid motion had an effect on the shape and size of assemblies. It was found that the nonequilibrium case was substantially different versus the equilibrium case, in both size and shape of the assembled structure. This dependence was related to the relative magnitudes of the electric-field driven convective motion of particles versus the fluid velocity. Fluid velocity magnitudes on the order of mm/s were predicted for 0.32 μm particles at 1% initial solids content, and the induced fluid velocity was found to be larger at the same voltage/initial volume fraction as the particle size decreased, owing to a larger contribution from entropic forces.
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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) | 0.000 | 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 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".