Bibliographic record
Abstract
Particle stability and coagulation are important aspects of colloidal systems. In the past, significant efforts have been made to simulate the interaction of particles for better design of colloidal systems and to improve processes dealing with colloidal systems. Despite their valuable analysis, past review papers discussed the interaction of smooth surfaces and particles. However, as particles have different surface morphologies, the interaction of particles and surfaces with rough surface morphologies is different from that of smooth particles. The present work summarized the numerical models for constructing particles and surfaces with different geometrical shapes. Also, it provides a comprehensive discussion of the modeling techniques used for understanding the interaction of particles with rough surface morphology in colloidal systems. It elaborates on the limitations and strengths of such mathematical simulations. Also, the current challenges, future directions, and potential application of such particles with different surfaces are described in this work comprehensively. This paper reviews various simulation methods for developing rough surfaces and particles and their interaction in different environments. It also discusses the advantages, disadvantages, and potential applications of each method, as well as future research directions in this field. • Interaction of roughly made surfaces was reviewed in this review paper. • Mathematical models for generating rough surfaces with different geometries were presented. • Models for understanding the interaction of roughly made surfaces with different topographies were presented. • Strengths and weaknesses of each model and potential future uses of models were discussed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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 | 0.001 |
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".