The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Cubic versus Spherical Magnetic Nanoparticles: The Role of Surface Anisotropy
Abstract
The magnetic properties of maghemite (gamma-Fe2O3) cubic and spherical nanoparticles of similar sizes have been experimentally and theoretically studied. The blocking temperature, T(B), of the nanoparticles depends on their shape, with the spherical ones exhibiting larger T(B). Other low temperature properties such as saturation magnetization, coercivity, loop shift or spin canting are rather similar. The experimental effective anisotropy and the Monte Carlo simulations indicate that the different random surface anisotropy of the two morphologies combined with the low magnetocrystalline anisotropy of gamma-Fe2O3 is the origin of these effects.
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Citations
Cites
Monodisperse FePt Nanoparticles and Ferromagnetic FePt Nanocrystal Superlattices
Sun, S., Murray, C. B., Weller, D., Folks, L., & Moser, A. (2000) · Science · doi:10.1126/science.287.5460.1989 · Cited by 6,137
Ultra-large-scale syntheses of monodisperse nanocrystals
Park, J., An, K., Hwang, Y., Park, J.-G., Noh, H.-J., Kim, J.-Y., Park, J.-H., Hwang, N.-M., & Hyeon, T. (2004) · Nature Materials · doi:10.1038/nmat1251 · Cited by 4,076
Microemulsion-assisted synthesis of tunable superparamagnetic composites
Tartaj, P., & Serna, C. J. (2002) · Chemistry of Materials · doi:10.1021/cm021214d · Cited by 110
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Review on magnetic nanoparticles for magnetic nanofluid hyperthermia application
Hedayatnasab, Z., Abnisa, F., & Daud, W. M. A. W. (2017) · Materials & Design · doi:10.1016/j.matdes.2017.03.036 · Cited by 565
Magnetic nanoparticles: recent advances in synthesis, self-assembly and applications
Singamaneni, S., Bliznyuk, V. N., Binek, C., & Tsymbal, E. Y. (2011) · Journal of Materials Chemistry · doi:10.1039/c1jm11845e · Cited by 537
Design strategies for shape-controlled magnetic iron oxide nanoparticles
Roca, A. G., Gutiérrez, L., Gavilán, H., Fortes Brollo, M. E., Veintemillas-Verdaguer, S., & Morales, M. D. P. (2019) · Advanced Drug Delivery Reviews · doi:10.1016/j.addr.2018.12.008 · Cited by 366
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Zhang, Q., Yang, X., & Guan, J. (2019) · ACS Applied Nano Materials · doi:10.1021/acsanm.9b00976 · Cited by 305
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Nemati, Z., Alonso, J., Rodrigo, I., Das, R., Garaio, E., García, J. Á., Orue, I., Phan, M.-H., & Srikanth, H. (2018) · The Journal of Physical Chemistry C · doi:10.1021/acs.jpcc.7b10528 · Cited by 256
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Wang, S., Xu, J., Li, W., Sun, S., Gao, S., & Hou, Y. (2022) · Chemical Reviews · doi:10.1021/acs.chemrev.1c00370 · Cited by 129
Nanoparticles toxicity: An overview of its mechanism and plausible mitigation strategies
Sharma, N., Kurmi, B. D., Singh, D., Mehan, S., Khanna, K., Karwasra, R., Kumar, S., Chaudhary, A., Jakhmola, V., Sharma, A., et al. (2024) · Journal of Drug Targeting · doi:10.1080/1061186x.2024.2316785 · Cited by 58
Metadata
- Authors
- Salazar-Alvarez, G., Qin, J., Šepelák, V., Bergmann, I., Vasilakaki, M., Trohidou, K. N., Ardisson, J. D., Macedo, W. A. A., Mikhaylova, M., Muhammed, M., Baró, M. D., & Nogués, J.
- Venue
- Journal of the American Chemical Society
- Publisher
- American Chemical Society (ACS)
- Published
- 2008-09-11
- Volume
- 130
- Issue
- 40
- Pages
- 13234-13239
- ISSN
- 0002-7863 (ISSN-L)
- 1520-5126
- OpenAlex
- W2086109578
- Cited by
- 266
- References
- 3
- FWCI
- 5.56
- Type
- journal article
- Language
- en
- Open access
- closed
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