Optimal Design and Characterization of Superparamagnetic Iron Oxide Nanoparticles Coated with Polyvinyl Alcohol for Targeted Delivery and Imaging
Bibliographic record
Abstract
Superparamagnetic iron oxide nanoparticles (SPION) with narrow size distribution and stabilized by polyvinyl alcohol (PVA) were synthesized. The particles were prepared by a coprecipitation technique using ferric and ferrous salts with a molar Fe3+/Fe2+ ratio of 2. Using a design of experiments (DOE) approach, the effect of different synthesis parameters (stirring rate and base molarity) on the structure, morphology, saturation magnetization, purity, size, and size distribution of the synthesized magnetite nanoparticles was studied by various analysis techniques including X-ray powder diffraction (XRD), thermogravimetric analysis (TGA) with differential scanning calorimetry (DSC) measurements, vibrating-sample magnetometer (VSM), transmission electron microscopy (TEM), UV-visible, and Fourier transform infrared (FT-IR) spectrometer. PVA not only stabilized the colloid but also played a role in preventing further growth of SPION followed by the formation of large agglomerates by chemisorption on the surface of particles. A rich behavior in particle size, particle formation, and super paramagnetic properties is observed as a function of molarity and stirring conditions. The particle size and the magnetic properties as well as particle shape and aggregation (individual nanoparticles, magnetic beads, and magnetite colloidal nanocrystal clusters (CNCs) are found to be influenced by changes in the stirring rate and the base molarity. The formation of magnetic beads results in a decrease in the saturation magnetization, while CNCs lead to an increase in saturation magnetization. On the basis of the DOE methodology and the resulting 3-D response surfaces for particle size and magnetic properties, it is shown that optimum regions for stirring rate and molarity can be obtained to achieve coated SPION with desirable size, purity, magnetization, and shape.
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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) | 0.000 | 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".