Shear‐induced aggregation of colloidal particles: A comparison between two different approaches to the modelling of colloidal interactions
Bibliographic record
Abstract
Abstract The process of shear‐induced aggregation of fully destabilized colloidal suspensions has been investigated by adopting a mixed deterministic‐stochastic modelling method. This method is based on a combination of a Monte Carlo algorithm, used to solve in a stochastic way the population balance equation for a purely aggregating suspension, and a Discrete Element Method, employed to simulate aggregation events in a fully predictive manner. The DEM was built in the framework of the well‐established Stokesian dynamics technique to get an accurate prediction of the hydrodynamic forces acting on the primary particles. Two different approaches were instead used to describe colloidal interactions: the first assumes primary particles to interact only by means of central forces; a second model assumes also tangential interactions to act on primary particles upon contact. To describe such interactions we adopted a spring‐like force model recently proposed by Becker and Briesen. Simulations were performed to ascertain the effect of these two different modelling approaches on the process of aggregation, showing that substantial differences appear in the predicted cluster morphology.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".