Role of Patterned Surface Charge Heterogeneity on Particle Deposition
Bibliographic record
Abstract
A finite element analysis of the fluid flow and the colloidal particle transport equations near a micropatterned charged substrate under radial impinging jet flow conditions is presented to investigate the charge heterogeneity effects on particle deposition. The particle Sherwood number representing the dimensionless particle deposition flux is obtained as a function of the radial distance from the stagnation point. The charge heterogeneity is modeled as concentric bands bearing positive and negative charges on the substrate. When a negatively-charged particle approaches such a charge heterogeneous substrate, it experiences an alternating attractive and repulsive force due to the presence of different charges on the substrate. Consequently, as the particle moves radially outward from the stagnation point, it experiences a periodic array of favorable (attractive) and unfavorable (repulsive) regions on the substrate, giving rise to an oscillatory trajectory. The numerical results obtained from the finite element model are in excellent agreement with existing theoretical and experimental values of deposition rates on homogeneous collector surfaces. However, the results for particle deposition over a heterogeneous substrate depict a significant deviation from those predicted by the patchwise heterogeneity model due to the coupled influence of hydrodynamic interactions and the surface chemical heterogeneity of the collector. The particles that do not deposit over an unfavorable repulsive band are convected to the next favorable band by the tangential velocity. This increases the particle concentration at the leading edge of each favorable band resulting in an increase in particle deposition over the favorable bands and the overall deposition rate on to the collector. Application of this phenomenon will be discussed in context of developing micropatterned surfaces with engineered particle capture properties.
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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 | 0.001 | 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".