Research Output

Conical Nanoparticles for Blood Disease Detection

  Metallic nanoparticles play an important role in the design of sensing platforms. In this paper, a new electromagnetic study for conical metal nanoparticles, working in the Near Infrared and Visible frequency regime, is proposed. The structures consist of inclusions, arranged in an array configuration, embedded in a dielectric environment. The aim of this work is to develop new analytical models, in order to describe the nanoparticles electromagnetic behavior in terms of extinction cross-section (absorption and scattering). The closed-form formulas link the conical nanoparticles geome- trical and electromagnetic parameters to their resonant frequency properties in terms of wavelength position, magnitude and bandwidth. The proposed models are compared to the numerical results and to the experimental ones, reported in literature. Good agreement is obtained. The proposed analytical formulas represent useful tools for sensing applications. For this reason, exploiting such models a new sensing platform able to detect different blood diseases is obtained. Nu- merical results confirm the capability of the proposed structure to be used as a sensing platform for medical diagnostics.

  • Type:

    Article

  • Date:

    31 December 2013

  • Publication Status:

    Published

  • Publisher

    Scientific Research Publishing, Inc,

  • DOI:

    10.4236/anp.2013.23036

  • ISSN:

    2169-0510

  • Library of Congress:

    TK Electrical engineering. Electronics Nuclear engineering

  • Dewey Decimal Classification:

    621.3 Electrical & electronic engineering

  • Funders:

    Edinburgh Napier Funded

Citation

Spada, L. L., Iovine, R., Tarparelli, R., & Vegni, L. (2013). Conical Nanoparticles for Blood Disease Detection. Advances in nanoparticles, 02(03), 259-265. doi:10.4236/anp.2013.23036

Authors

Keywords

Conical Nanoparticles; Analytical Models; Sensitivity; Sensing Platform; Blood Diseases

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    Copyright © 2013 Luigi La Spada et al. This is an open access article distributed under the Creative Commons Attribution License,
    which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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