Predicting the interaction between nanoparticles in shear flow using lattice Boltzmann method and Derjaguin–Landau–Verwey–Overbeek (DLVO) theory
Bibliographic record
Abstract
The functionality and performance of colloidal suspensions used in catalyst layer preparation and biomedical applications are largely dependent on the interaction between nanoparticles in colloidal suspension systems. Previous models (e.g., collision model) usually rely on an artificial repulsive force as the sole interaction between nanoparticles to prevent overlapping, but fail to capture the agglomeration or reveal the effect of solvents. In this study, the Derjaguin–Landau–Verwey–Overbeek (DLVO) theory is implemented in conjunction with a lattice Boltzmann-smoothed profile method developed to simulate the dynamic solid–fluid and particle–particle interactions between nanoparticles in shear flow. Both aqueous and non-aqueous solvents are considered. The model consists of an attractive van der Waals force and repulsive electrostatic and Born forces in aqueous solvents and is modified for non-aqueous solvents by replacing the repulsive electrostatic force by Coulombic repulsion. The numerical model is validated against a benchmark analytic solution for the motion of one nanoparticle in shear flow. For two-particle systems, physically representative simulations are obtained with the DLVO models, resulting in nanoparticles that remain attached or eventually detach depending on a critical particle Reynolds number. Furthermore, the DLVO models properly resolve the effect of solvents on nanoparticle motion. The improved representation of inter-particle interactions achieved with the DLVO and modified-DLVO models provides a physically consistent approach to simulate and investigate agglomeration and dispersion in colloidal suspensions.
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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 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".