Rheological Properties of Superparamagnetic Iron Oxide Nanoparticles
Bibliographic record
Abstract
The present study focuses on the rheological properties of polyethylene glycol (PEG) modified, positively charged, and negatively charged superparamagnetic iron oxide nanoparticles (SPIONs) at different temperatures. We hypothesized that the surface properties of these nanoparticles in the water did not affect their rheological properties. These nanoparticles had not the same surface properties as SPIONs-PEG had not to charge on their surface whereas positively charged and negatively charged ones with amine and carboxyl groups as their surfaces had positive and negative surface charges, respectively. However, their rheological behaviors were not different from each other. The comparative rheological study of SPIONs revealed their pseudo-Newtonian behavior. The viscosity of SPIONs decreased with the increase in temperature. At low shear rates, the shear stress of SPIONs was independent of rate and increased with the increase of rate. Moreover, at high shear rates, the shear stress for PEG-SPIONs was more than those for positively charged and negatively charged SPIONs. These measurements also revealed that at high shear rates, the shear stress of samples decreased with the increase of temperature. The shear stress of samples decreased with the increase of shear strain and the temperature. We also observed that all the samples had the same amount of shear strain at each shear stress, which indicated the exact resistance of SPIONs to deformation. Furthermore, the shear modulus decreased with time for these nanoparticles. These results suggest that these nanoparticles are promising candidates with appropriate properties for fluid processing applications and drug vectors in biomedical applications.
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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".