Finite system approximation in colloidal systems with aggregation and break-up
Bibliographic record
Abstract
In this Thesis, reactive multiparticle collision dynamics (RMPC) is used to simulate red blood cell cluster concentration profiles in the presence of aggregation, as well as when aggregation and break-up are present together. RMPC dynamics involves local collisions, reactions and free-streaming of particles. Reactive mechanisms are used to model the aggregation and break-up of particles. This analogy is motivated by a system of ODES called the Smoluchowski differential equations that have been used to model aggregating systems in the well-mixed case. Exact solutions for the (infinite) systems of ODEs for the Smoluchowski equation are compared to a numerical ODE system solution where the maximum cluster size is N (finite) rather than infinite as assumed in the Smoluchowski equation. The numerical ODE solution is compared to the exact solution in the infinite system when the maximum cluster size is 20 or less. Stochastic RMPC simulations are performed when the maximum cluster size N = 3, and the simulation domain is a cubic volume subject to periodic boundary conditions. Constant and equal aggregation and break-up rates are considered, as well as much smaller aggregation rates compared to break-up rates and vice-versa. Two different initial conditions are considered: monomer-only, as well as non-zero initial concentrations for clusters of all sizes. The simulation for the RMPC (finite), numerical ODE (finite) and exact (infinite) can be shown to have good agreement in the equilibrium concentrations of the chemical species in the system in some cases, although agreement is poor in other cases. This work is an important stepping stone that can be expanded to incorporate flow conditions into the particle dynamics in future work, so as to more accurately investigate pathological conditions including atherosclerotic plaque formation.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".