Effect of Membrane Surface Roughness on Colloid−Membrane DLVO Interactions
Bibliographic record
Abstract
Recent experimental investigations suggest that interaction of colloidal particles with polymeric membrane surfaces is influenced by membrane surface morphology (roughness). To better understand the consequences of surface roughness on colloid deposition and fouling, it is imperative that models for predicting the Derjaguin−Landau−Verwey−Overbeek (DLVO) interaction energy between colloidal particles and rough membrane surfaces be developed. We present a technique of reconstructing the mathematical topology of polymeric membrane surfaces using statistical parameters derived from atomic force microscopy roughness analyses. The surface element integration technique is used to calculate the DLVO interactions between spherical colloidal particles and the simulated (reconstructed) membrane surfaces. Predictions show that the repulsive interaction energy barrier between a colloidal particle and a rough membrane is lower than the corresponding barrier for a smooth membrane. The reduction in the energy barrier is strongly correlated with the magnitude of surface roughness. It is further suggested that the valleys created by the membrane surface roughness produce wells of low interaction energy in which colloidal particles may preferentially deposit.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".