Bibliographic record
Abstract
Abstract Emulsion polymerization has been practiced for nearly a century, but its basic mechanisms were not understood until much later. The product of this polymerization method is called a latex and its vast array of properties continues to be developed by utilizing the nanoparticle characteristics of such aqueous dispersions (∼1018particles/L). Due to the compartmentalization of the active, free radicals within separate particles, one can achieve high reaction rates and high polymer molecular weights at the same time—a unique feature of emulsion polymerization. At the same time, the resulting latex has low viscosity and high heat capacity, even at 50% polymer content, because water is the dispersing agent. Over the years, latex technology has evolved to include composite polymer nanoparticles with two or more phase‐separated regions within them. Further extensions continue to be made that result in hybrid latex particles in which one component is not created via standard emulsion polymerization processes (eg, metal oxides, alkyd resin, and polyurethane), but a second component is created in that manner. Other new processing techniques have also been developed to avoid some of the mechanistic restrictions of standard emulsion polymerization (particle nucleation via micellar initiation, poor water solubility of some vinyl monomers) leading to “miniemulsion polymerization” systems. Further extensions have resulted in “microemulsion polymerizations” in which the final, dispersed particle sizes are smaller than the wavelength of visible light.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.039 | 0.024 |
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".