Green exfoliation of van der Waals–based magnetic nanocomposites for hyperthermia applications
Bibliographic record
Abstract
We present the development of novel nanocomposites consisting of van der Waals (vdW) materials (WS 2 , MoS 2 , MoSe 2 , TiS 2 , and graphene), maghemite nanoparticles (MNPs), and a gum arabic (GA) matrix, optimized for magnetic hyperthermia applications. These superparamagnetic nanocomposites were comprehensively investigated using a range of advanced characterization techniques. It was found that the incorporation of MNPs enhances the exfoliation efficiency of vdW materials. Transmission electron microscopy revealed that the MNPs, with an average diameter of 〈D TEM 〉 = 8.3 ± 0.1 nm, are nearly spherical and uniformly anchored on the surfaces of the vdW material flakes. Raman spectroscopy confirmed the presence of characteristic 2D material signatures and verified the formation of magnetic nanocomposites with varying layer numbers. Zeta potential measurements indicated high colloidal stability, which is essential for biomedical applications. Magnetic measurements confirmed the superparamagnetic nature of the vdW-integrated nanocomposites, showing reduced saturation magnetization, increased coercivity, and a shifted blocking temperature due to dipole–dipole interactions influenced by the presence of vdW materials. The highest specific absorption rate (SAR) values recorded were 21.4 W/g for Graphene@GA@MNPs, 20.6 W/g for WS 2 @GA@MNPs, and 23.3 W/g for TiS 2 @GA@MNPs. Magnetic hyperthermia tests demonstrated efficient heat generation under alternating magnetic fields, reinforcing their potential for biomedical applications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.001 | 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 teacher head, 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".