Particle Tracking Model for Colloid Transport near Planar Surfaces Covered with Spherical Asperities
Bibliographic record
Abstract
This paper proposes a Lagrangian particle tracking model (PTM) for predicting colloid transport near a planar substrate containing protruding spherical asperities in the presence of shear flow. The fluid flow field around such a physically heterogeneous substrate is obtained from a numerical solution of the Stokes equations. A simple approximation of the particle-substrate hydrodynamic interactions is developed based on the universal hydrodynamic correction functions. The model is employed to quantitatively predict how the presence of a spherical asperity on a macroscopically planar substrate can influence deposition of particles on the substrate in shear flow. Some simulation results depicting the deposit morphologies on an array of spherical asperities are also presented. Results from the PTM reveal that (i) asperities act as attractive "beacons", pulling particles closer to the composite substrate regardless of whether or not it is favorable to deposition; (ii) asperities can also act as additional collectors, increasing the available surface area onto which particles can deposit; and (iii) particles deposit on the "peaks" of the asperities under favorable conditions. From a mainly hydrodynamic standpoint, these observations indicate that physical heterogeneity on surfaces can have significant influence on particle deposition. The modification of the flow field due to the substrate's geometry, coupled with the modifications due to hydrodynamic retardation of the particle, lead to large variations of deposition probabilities. Therefore, assuming perfectly smooth collectors to compute the flow field may lead to errors in predicting deposition phenomena on physically heterogeneous collectors.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".