Multiphysics Flow Modeling and in Vitro Toxicity of Iron Oxide Nanoparticles Coated with Poly(vinyl alcohol)
Bibliographic record
Abstract
This study investigated the behavior of ferrofluids containing superparamagnetic iron oxide nanoparticles (SPION) of various compositions for potential applications in drug delivery and imaging. To ensure biocompatibility, the interaction of these SPION with two cell lines (adhesive and suspended) was also investigated using an MTT (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide) assay. The cell lines studied were primary mouse connective tissue cells (adhesive) and human leukemia cells (suspended). SPION were synthesized with a co-precipitation method under different stirring rates and NaOH molarities. The SPION demonstrated a range of magnetic saturations due to their different shapes, which included magnetite colloidal nanocrystal clusters (CNC’s), magnetic beads, and single-coated nanoparticles. All synthesized SPION maintained reasonable cell viability following exposure to cells. Flow cytometer tests showed that no apoptosis took place in cells exposed to SPION. A multiphysics numerical model was developed to study the dynamic behavior of ferrofluids containing the SPION in a blood vessel while under an externally applied magnetic field. Simulation results suggest that the SPION magnetic properties and the strength of the external field are important factors in determining both the shape and amplitude of the resulting ferrofluid velocity field.
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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 | 0.001 |
| 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 | 0.001 | 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".