Porous Silicon Nanoparticles

  • Hélder A. SantosEmail author
  • Ermei Mäkilä
  • Luis M. Bimbo
  • Patrick Almeida
  • Jouni Hirvonen


Micro- and nano-based technologies are presently recognized as promising potential tools for drug delivery applications in almost every field of health sciences, aiming to overcome the adverse physicochemical properties of conventional drug molecules, which often lead to poor drug bioavailability. A large amount of the new chemical entities developed by the pharmaceutical industry are poorly water-soluble compounds, which in order to subsist as efficient drugs with improved and controllable in vivo behaviour require the aid of more advanced technologies. In this context, porous silicon (PSi) nanocarriers have received considerable attention for the delivery of a wide range of therapeutics, particularly due to their excellent in vivo biocompatibility, easy surface chemical modification and easy control over their porous network structure. The literature has extensively demonstrated the successful use of PSi for controlling the loading and release of poorly water-soluble drugs; however, in this chapter we will mainly focus on the applications of the PSi-based nanoparticles for biomedical applications. In this chapter, we start by addressing the issues of poorly water-soluble drugs and then introduce PSi-based materials as potential drug carriers for such drugs. We then highlight the fabrication methodology of PSi materials, the drug loading and release, and present several examples of the significant potential of PSi in biomedical imaging and in drug delivery applications. These applications exploit these promising features of PSi for future translation to the clinic. We will conclude the chapter with a brief overview of our visions of the future of the PSi nanomaterials and their implications in the pharmaceutical and biomedical field.


Drug Release Porous Silicon Drug Molecule Drug Delivery Application Biopharmaceutical Classification System Class 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



Hélder A. Santos acknowledges the Academy of Finland (project numbers 252215 and 256394) and the European Research Council under the European Union’s Seventh Framework Programme (FP7/2007–2013)/ERC Grant agreement number 310892 for financial support. Luis M. Bimbo acknowledges the Finnish Cultural Foundation for financial support.


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Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Hélder A. Santos
    • 1
    Email author
  • Ermei Mäkilä
    • 1
    • 2
  • Luis M. Bimbo
    • 1
  • Patrick Almeida
    • 1
  • Jouni Hirvonen
    • 1
  1. 1.Division of Pharmaceutical Technology, Faculty of PharmacyUniversity of HelsinkiHelsinkiFinland
  2. 2.Laboratory of Industrial Physics, Department of Physics and AstronomyUniversity of TurkuTurkuFinland

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