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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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 teacher head, 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".