A robust computational method based on the thermodynamic approach for determining monomer partitioning in emulsion polymerization systems
Bibliographic record
Abstract
Abstract A robust computational framework based on the thermodynamic approach for determining monomer partitioning in monodisperse/polydisperse multi‐component emulsion systems was introduced. The numerical methods were applied to determine monomer partitioning in three monodisperse and one hypothetical polydisperse multi‐monomer emulsion systems. Larger particle size promotes the swelling ability of particles and increases the monomers content ratio for monomer with lower solubility in particle phase. For polydisperse systems, there is a strong particle size dependency of polymer volume fraction and the volumetric ratio of the monomers in particles, even for large particles and/or under partial swelling condition. In monodisperse systems, the particle size independency of monomers' volumetric ratio is only valid for large enough particles and/or partial swelling conditions. The results show that the prediction of the thermodynamic model can be brought close to reasonable results only with values of interfacial tensions, which are several times higher than the experimentally measured data.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.002 | 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".