Effects of Coating and Stirring on Superparamagnetic Iron Oxide Nanoparticles Size and Magnetic Characteristics
Why this work is in the frame
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Bibliographic record
Abstract
Considering the importance of nanoparticles physico-chemical properties in biomedical applications, we intend to describe and compares the results of fiveexperimental studies including: uncoated magnetic nanoparticles (MNPs), MNP + polyvinyl alcohol (PVA), MNP + amorphous silica (SiO 2 ) + gold (Au), and MNP + Au only. Controlled co-precipitation technique under N 2 gas is used to prevent undesirable critical oxidation of Fe 2+ .For uncoated Fe 3 O 4 NPs with saturation magnetization (M s ) range of (40-100) emu/g, smaller particles are synthesized by decreasing the NaOH concentration and increasing the stirring speed with the smallest value corresponding to 7.5 nm using 0.9 M of NaOH at 1500 rpm. The coatingprocess is done in four separate steps as follows:(i) the stable magnetic fluid containing well-dispersed Fe 3 O 4 /PVA nanocomposites which indicates a fast magnetic response with the smallest value of 7.5 nm using 0.9 M of NaOH at 750 rpm and M s of 50 emu/g, (ii) the synthesized Fe 3 O 4 NPs are stabilized using trisodium citrate (TSC) coating and then covered by SiO 2 layer using Stober method with the smallest value of 50 nm using 0.9 M of NaOH at 750 rpm and M s of 30 emu/g, (iii) small gold colloids (1-3 nm) are synthesized using Duff method and covered the amino functionalized particle surface of Fe 3 O 4 /SiO 2 nanoshells with the smallest value of 85 nm using 0.9 M of NaOH at 750 rpm and M s of 1.3 emu/g, (iv) also, bare superparamagneticIron oxide NPs (SPIONs) are covered by a thin layer of gold alone with the smallest value of 16 nm using 0.9 M of NaOH at 1500 rpm and M s of 12 emu/g. Magnetic properties and size of nanoshells are assessed using vibrating sample magnetometer (VSM)and transmission electron microscope (TEM). Furthermore, M s of 7.5 nm magnetite is high enough to be used as contrast agent for photoacoustic (PAI) and magnetic resonance imaging (MRI).
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 it