Surface Roughness Impacts on Granular Media Filtration at Favorable Deposition Conditions: Experiments and Modeling
Bibliographic record
Abstract
Column tests were conducted to investigate media roughness impacts on particle deposition in absence of an energy barrier (i.e., high ionic strength). Media/collector surface roughness consistently influenced colloid deposition in a nonlinear, nonmonotonic manner such that a critical roughness size associated with minimum particle deposition could be identified; this was confirmed using a convection-diffusion model. The results demonstrate that media surface roughness size alone is inadequate for predicting media roughness impacts on particle deposition; rather, the relative size relationship between the particles and media/collectors must also be considered. A model that quantitatively considers media surface roughness was developed that described experimental outcomes well and consistently with classic colloid filtration theory (CFT) for smooth surfaces. Dimensionless-scaling factors froughness and fPCIF were introduced and used to develop a model describing particle deposition rate (kd) and colloid attachment efficiency (α). The model includes fitting parameters that reflect the impact of critical system characteristics such as ionic strength, loading rate, hydrophobicity. Excellent agreement was found not only between the modeled outcomes for colloid attachment efficiency (α) and experimental results from the column tests, but also with experimental outcomes reported elsewhere. The model developed herein provides a framework for describing media surface roughness impacts on colloid deposition.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".