Preparation and characterization of 2‐hydroxyethyl methacrylate‐based porous copolymeric particles
Bibliographic record
Abstract
Abstract 2‐Hydroxyethyl methacrylate was copolymerized with three different comonomers, methyl methacrylate (MMA), styrene (St), and N‐vinyl‐2‐pyrrolidone (NVP), respectively, to prepare porous particles crosslinked using ethylene glycol dimethacrylate (EGDMA) in the presence of an organic solvent, 1‐octanol (porogen), by means of suspension copolymerization in an aqueous phase initiated by 2,2‐azobisisobutyronitrile. Nano‐pores were observed in the particles. The pore size and the swelling properties of these particles can be controlled by changing comonomers or adjusting the crosslinker or porogen concentration. A lower crosslinker or porogen concentration favors generating smaller pores, whereas a higher concentration of a hydrophilic comonomer, higher concentration of crosslinker, and higher porogen volume ratio promote the generation of larger pores. In addition, the effects of the porous characteristics on the swelling properties were explored. The swelling capacity of the porous particles is reduced with the increase in the crosslinker concentration; however, there is a critical porogen volume ratio, in which the maximal swelling capacity is reached. Higher porosity in the particles and higher amount of hydrophilic comonomer favor a higher swelling capacity of the particles. © 2007 Wiley Periodicals, Inc. J Appl Polym Sci 2007
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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".