Impact of calcination temperature on the spin–spin relaxation time (<i>T</i><sub>2</sub>) of MgFe<sub>2</sub>O<sub>4</sub> nanoparticles (in vitro)
Bibliographic record
Abstract
MgFe2O4 nanoparticles were prepared by the sol–gel method at calcination temperatures of 300 and 500 °C. Then, the effect of calcination temperature on their structural, magnetic, and cytotoxic properties was investigated. In this regard, X-ray diffraction (XRD), field emission scanning electron microscopy (FESEM), and Fourier transform infrared spectroscopy (FTIR) techniques were used to study structural features, vibrating sample magnetometry (VSM) and electron paramagnetic resonance spectroscopy (EPR) methods were used to evaluate the magnetic properties, and the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) test was used to evaluate the cytotoxicity. XRD and FESEM results showed that the particle size should increase with increasing calcination temperature. FTIR spectra indicated the presence of absorption bands in the range 390–560 cm−1 at both calcination temperatures, which is a common feature of spinel ferrite. Also, the VSM analysis showed that the superparamagnetic property decreases with increasing calcination temperature. Spin–spin relaxation time ( T2) was evaluated as one of the important parameters in increasing the quality of magnetic resonance imaging scans by EPR. EPR results showed that the T2 increases with increasing calcination temperature. The cytotoxic effects (MTT test) of MgFe2O4 nanoparticles at different concentrations on normal human fibroblast cells (HU-02) showed dose-dependent cell death. This study showed that lowering the calcination temperature can improve the spin–spin contrast ( T2).
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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".