Preparation of poly(methyl methacrylate)‐Silica nanoparticles via differential microemulsion polymerization and physical properties of <scp>NR</scp>/<scp>PMMA</scp>‐<scp>S</scp>i<scp>O</scp><sub>2</sub> hybrid membranes
Bibliographic record
Abstract
Poly(methy methacrylate) (PMMA)‐SiO 2 nanoparticles were prepared via differential microemulsion polymerization. The effects of silica loading, surfactant concentration, and initiator concentration on monomer conversion, particle size, particle size distribution, grafting efficiency, and silica encapsulation efficiency were investigated. A high monomer conversion of 99.9% and PMMA‐SiO 2 nanoparticles with a size range of 30 to 50 nm were obtained at a low surfactant concentration of 5.34 wt% based on monomer. PMMA‐SiO 2 nanoparticles showed spherical particles with a core‐shell morphology by TEM micrographs. A nanocomposite membrane from natural rubber (NR) and PMMA‐SiO 2 emulsion was studied for mechanical and thermal properties and pervaporation of water‐ethanol mixtures. PMMA‐SiO 2 nanoparticles which were uniformly dispersed in NR matrix, significantly enhanced mechanical properties and showed high water selectivity in permeate flux. Thus, the NR/PMMA‐SiO 2 hybrid membranes have great potential for pervaporation process in membrane applications. POLYM. ENG. SCI., 2017. © 2017 Society of Plastics Engineers
Stored with the screening record, where it is evidence for the labels above.
How this classification was reachedexpand
The three-model screen
all 5,600 screened works →All three models called this out of scope.
Polymer science on PMMA-silica nanoparticles and hybrid membranes; the object is a material.
The study investigates polymer nanoparticles and membrane properties, not research practice.
Materials science study of PMMA-silica nanoparticles and membranes; not about research itself.
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) | 0.000 | 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".