In Vitro Study: Synthesis and Evaluation of Fe<sub>3</sub>O<sub>4</sub>/CQD Magnetic/Fluorescent Nanocomposites for Targeted Drug Delivery, MRI, and Cancer Cell Labeling Applications
Bibliographic record
Abstract
In the present study, first, Fe 3 O 4 nanoparticles were functionalized using glutaric acid and then composited with CQDs. Doxorubicin (DOX) drug was loaded to evaluate the performance of the nanocomposite for targeted drug delivery applications. The XRD pattern confirmed the presence of characteristic peaks of CQDs and Fe 3 O 4 . In the FTIR spectrum, the presence of carboxyl functional groups on Fe 3 O 4 /CQDs was observed; DOX (positive charge) is loaded onto Fe 3 O 4 /CQDs (negative charge) by electrostatic absorption. FESEM and AFM images showed that the particle sizes of Fe 3 O 4 and CQDs were 23–75 and 1–3 nm, respectively. The hysteresis curves showed superparamagnetic properties for Fe 3 O 4 and Fe 3 O 4 /CQDs (57.3 and 8.4 emu/g). The Fe 3 O 4 hysteresis curve showed superparamagnetic properties (Ms and Mr: 57.3 emu/g and 1.46 emu/g. The loading efficiency and capacity for Fe 3 O 4 /CQDs were 93.90% and 37.2 mg DOX/g MNP, respectively. DOX release from Fe 3 O 4 /CQDs in PBS showed pH-dependent release behavior where after 70 h at pH 5 and 7.4, about 50 and 21% of DOX were released. Fluorescence images of Fe 3 O 4 /CQD-treated cells showed that Fe 3 O 4 /CQDs are capable of labeling MCF-7 and HFF cells. Also, T 2 -weighted MRI scans of Fe 3 O 4 /CQDs in water exhibited high r 2 relaxivity (86.56 mM –1 S –1 ). MTT assay showed that DOX-loaded Fe 3 O 4 /CQDs are highly biocompatible in contact with HFF cells (viability = 95%), but they kill MCF-7 cancer cells (viability = 45%). Therefore, the synthesized nanocomposite can be used in MRI, targeted drug delivery, and cell labeling.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".