PREPARATION OF MAGNETIC COMPOSITE MICROSHERES BY SOAP-FREE EMULSION POLYMERIZATION IN THE PRESENCE OF 1,1-DIPHENYLETHENE
Bibliographic record
Abstract
The magnetic composite microspheres,which had no surface active agents on the surface and were more suitable for use of biomedicine,were prepared by a simple method——DPE method. In the preparation of 1,1-diphenylethene ( DPE ),methyl methacrylate ( MMA ) and acrylic acid ( AA ) took place soap-free polymerization,and the living short copolymer chains,which could chelate with Fe3O4,were prepared. Then Fe3O4were added,the living short copolymer chains were chelated to the surface of Fe3O4. The subsequent polymerization could take place on the surface of the Fe3O4,then the magnetic composite microspheres were prepared. The effects of AA,DPE,Fe3O4 and initiator were investigated in detail. With the increase of AA,DPE and initiator,the morphology of magnetic composite microspheres improved and then declined. The morphology of magnetic composite microspheres had no big change with addition of Fe3O4,but the magnetic content increased obviously. The magnetic composite microspheres,prepared in optimum conditions,were characterized by transmission electronic microscopy ( TEM ),thermogravimetric analysis ( TGA ) and vibrating sample magnetometer (VSM). The results showed that for these magnetic composite microspheres the magnetic content was about 20% ,the specific saturation magnetization was about 32. 2 emu /g,the average size was 265 nm and there were no any impurity on their surface.
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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) | 0.001 | 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 | 0.001 |
| 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".