A sensitive fluorescence method for monitoring the kinetics of microemulsion polymerization
Bibliographic record
Abstract
We present a fluorescence method that allows one to monitor the kinetics of microemulsion polymerization of very low monomer contents (water-to-monomer ratio can readily be superior to 1000). The microemulsion polymerization of methyl methacrylate (MMA) was investigated using N-(2-anthracene)methacrylamide (AnMA) as the probe whose fluorescence emission intensity was proportional to the conversion of MMA into the polymer. The real-time-measurement results show that in the regime of very low monomer contents, the surfactant exerted a profound effect on the kinetic process. In a microemulsion containing 0.1 wt% of MMA with respect to water, with the anionic surfactant of sodium dodecyl sulfate (SDS), the fast polymerization was preceded by an induction period whose length increased with reducing the concentration of the water-soluble initiator of potassium persulfate (KPS). By contrast, with the non-ionic surfactant of polyoxyethylene (20) oleyl ether (Brij98), the induction period was short and the decrease in the KPS concentration mainly resulted in a decrease of the reaction rate. The unprecedented sensitivity of this fluorescence method made it possible to access kinetic data of microemulsion polymerization with very low monomer contents for the first time, providing new insight into the effects of surfactant and initiator on this heterophase polymerization process.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".