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author:("salek, Ota")
1.  Self-ordered TiO2 quantum dot array prepared via anodic oxidation 
Nanoscale Research Letters  2012;7(1):123.
The template-based methods belong to low-cost and rapid preparation techniques for various nanostructures like nanowires, nanotubes, and nanodots or even quantum dots [QDs]. The nanostructured surfaces with QDs are very promising in the application as a sensor array, also called 'fluorescence array detector.' In particular, this new sensing approach is suitable for the detection of various biomolecules (DNA, proteins) in vitro (in clinical diagnostics) as well as for in vivo imaging.
The paper deals with the fabrication of TiO2 planar nanostructures (QDs) by the process of titanium anodic oxidation through an alumina nanoporous template on a silicon substrate. Scanning electron microscopy observation showed that the average diameter of TiO2 QDs is less than 10 nm. Raman spectroscopic characterization of self-organized titania QDs confirmed the presence of an anatase phase after annealing at 400°C in vacuum. Such heat-treated TiO2 QDs revealed a broad emission peak in the visible range (characterized by fluorescence spectroscopy).
PMCID: PMC3305443  PMID: 22333295
quantum dots; biosensing; TiO2; template methods; nanoporous mask
2.  Atomic force microscopy analysis of nanoparticles in non-ideal conditions 
Nanoscale Research Letters  2011;6(1):514.
Nanoparticles are often measured using atomic force microscopy or other scanning probe microscopy methods. For isolated nanoparticles on flat substrates, this is a relatively easy task. However, in real situations, we often need to analyze nanoparticles on rough substrates or nanoparticles that are not isolated. In this article, we present a simple model for realistic simulations of nanoparticle deposition and we employ this model for modeling nanoparticles on rough substrates. Different modeling conditions (coverage, relaxation after deposition) and convolution with different tip shapes are used to obtain a wide spectrum of virtual AFM nanoparticle images similar to those known from practice. Statistical parameters of nanoparticles are then analyzed using different data processing algorithms in order to show their systematic errors and to estimate uncertainties for atomic force microscopy analysis of nanoparticles under non-ideal conditions. It is shown that the elimination of user influence on the data processing algorithm is a key step for obtaining accurate results while analyzing nanoparticles measured in non-ideal conditions.
PMCID: PMC3212053  PMID: 21878120

Results 1-2 (2)