Synthesis of PMMA Microparticles with a Narrow Size Distribution by Photoinitiated RAFT Dispersion Polymerization with a Macromonomer as the Stabilizer
Bibliographic record
Abstract
Macromonomers can serve as efficient and effective stabilizers for dispersion polymerization of monomers such as styrene and methyl methacrylate, but the size distributions of the polymer microparticles obtained tend to be broad. We are interested in functional microbeads which can be used for immunoassays, where the size distribution has to be very narrow. We report a photoinitiated RAFT dispersion polymerization of methyl methacrylate (MMA) in ethanol–water mixtures, with methoxy-poly(ethylene glycol) methacrylate ( M n = 2000 g/mol, EO 45 ) as the reactive steric stabilizer. We identify reaction conditions where one can obtain PMMA microspheres with coefficient of variation in the particle diameter (CV d ) less than 3%. Carboxy-functional PMMA microspheres were obtained by a two-stage (seeded) polymerization with methacrylic acid (MAA) added as a comonomer in the second stage. We show that the functional microspheres prepared in this way are effective substrates for the covalent attachment of proteins such as BSA and IgG immunoglobulins. In one set of experiments with a dye-labeled secondary antibody, we found that we could detect 10 4 IgGs per PMMA microbead.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".