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Improving the Heating Efficiency of Iron Oxide Nanoparticles by Tuning Their Shape and Size
Open accessAbstract
Magnetic nanoparticle-mediated hyperthermia is a very promising therapy for cancer treatment. In this field, superparamagnetic iron oxide nanoparticles have been commonly employed because of their intrinsic biocompatibility, but they present some limitations that restrict their heating efficiency (specific absorption rate, SAR). Therefore, we have investigated how tuning the size and shape of these iron oxide nanoparticles can be useful to enhance their hyperthermia responses. Monodisperse and crystalline iron oxide nanoparticles have been synthesized by thermal decomposition in two different shapes (spheres and cubes) in a wide range of sizes, ∼10–100 nm. We have thoroughly characterized them both structurally (X-ray diffraction and transmission electron microscopy) and magnetically (physical property measurement system), and then we have analyzed their heating efficiency using a combination of calorimetric and AC magnetometry measurements (0–800 Oe, 300 kHz). We have been able to delimit a range of optimum sizes to maximize the heating efficiency of these nanoparticles depending on their shape. We find that the nanospheres exhibit the highest heating efficiency for sizes around 30–50 nm, while the nanocubes show a sharp increase in the heating efficiency around 30–35 nm. The SAR variation has been related to the magnetic anisotropy of the nanoparticles that depends on their size, shape, arrangement, and dipolar interactions.
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Metadata
- Authors
- Nemati, Z., Alonso, J., Rodrigo, I., Das, R., Garaio, E., García, J. Á., Orue, I., Phan, M.-H., & Srikanth, H.
- Venue
- The Journal of Physical Chemistry C
- Publisher
- American Chemical Society (ACS)
- Published
- 2018-01-17
- Volume
- 122
- Issue
- 4
- Pages
- 2367-2381
- ISSN
- 1932-7447 (ISSN-L)
- 1932-7455
- OpenAlex
- W2781746665
- Cited by
- 256
- References
- 87
- FWCI
- 11.14
- Type
- journal article
- Language
- en
- Open access
- green
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Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.