Modelling and Simulation of Complex Aspects of Multicomponent Emulsion Polymerization
Bibliographic record
Abstract
The focus of this work is the refinement of a general mechanistic simulator for multi‐component free radical emulsion polymerization. The effort includes three main areas of simulator development, namely, model development, database development and simulator verification. The model is general and can predict the dynamic evolution of emulsion polymerizations for a variety of monomer systems while giving the user as many “model options” as possible for fine tuning. The model has been extensively tested for several copolymerization systems including combinations of the monomers, styrene, methyl methacrylate, methyl acrylate, butyl acrylate, acrylonitrile, 2‐ehtyl hexyl acrylate and vinyl acetate. The simulator has been developed using a mechanistic framework that is analogous to the multicomponent free radical bulk and solution polymerization model developed in Gao and Penlidis (Citation1996&1998) and is a continuation of Gao and Penlidis (Citation[2002]), which explored emulsion homopolymerization and preliminary copolymerization modelling. The model includes a rigorous thermodynamic approach for determining monomer partitioning, the inclusion of both homogeneous and micellar particle nucleation as well as the ability to simulate various reactor configurations including batch and semi‐batch operation. Database items used in the simulator are chosen based on direct experimental data (when available) or from analogous situations and parameter estimations. The model has been developed in a general fashion such that a monomer, initiator, emulsifier, transfer agent etc. can be added to the database at any time. Furthermore, the model has been extended to predict particle size distribution of the resulting emulsion. This model has been tested with many case studies against a variety of experimental data and can be used for design of experiments for production of emulsions with customized distributions. #This paper is dedicated to Professor Gary W. Poehlein whose papers had always something new to teach us, both practical and theoretical.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".