Effects of Coating and Stirring on Superparamagnetic Iron Oxide Nanoparticles Size and Magnetic Characteristics
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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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 | 0.001 | 0.002 |
| 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 | 0.000 |
| 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".