The application of a positive or negative local bias to the surface of La[sub]{1.975}Sr[sub]{0.025}CuO[sub]{4+[delta]} (LSCO) crystal by a conducting atomic force microscope tip results in accumulation of a (positive or negative respectively) metastable charge on the surface. The surface charge initially shows diffusive dynamics with a timescale of hours, but thereafter it is shown to be stable for months. The charged regions are found by Auger electron spectroscopy to have a different stoichiometry from the surrounding material. A part from fundamental implications for the heterostructure device construction, such surface charge manipulation could lead to AFM nanopatterning of superconducting nanoscale device and applications memories.
COBISS.SI-ID: 899242
Because of their unique mechanical properties, shape memory and super plasticity, durability and biocompatibility, Ni-Ti alloys are the most important memory shape alloys for applications in a wide range of medical implants and devices. These alloys contain approximately equal Ni and Ti atomic concentrations, meaning medical applications are still hindered by the concern for the release of Ni into surrounding tissues. We have studied the surface composition of a melt-spun Ni-Ti shape memory alloy using different surface analytical techniques, namely, Auger electron spectroscopy and X-ray photoelectron spectroscopy, before and after testing its biocompatibility in vitro. We have found that the surface consists of an oxide layer of 10 to 20nm thickness, composed of Ti oxide and some Ni oxide with metallic Ni inhomogeneously distributed in the subsurface region.
COBISS.SI-ID: 15760150
One of the constituent elements in Auger electron spectroscopy (AES) spectra that make their automatic analysis next to impossible is the background. To overcome this obstade and enable further processing, the background removal stage must provide the clear enough data for the techniques capable of automatically extracting the requested information from the AES spectra. It is also known that the background in AES spectra contains further information regarding the composition of the sample studied. Thus, although removed, this background should not be disregarded. In our work, the neural networks for data modeling or background function approximation were used. Neural networks were used for the determination of the general shape of the background in AES spectra. Neural networks are model-less approximators, meaning that they are capable of accomplishing the approximation tasks regardiess of any prior knowledge on the nature of the modeled system. According to our analysis, three distinctive parts of the background in the spectra were proposed. One of them is the so-called peak base. The use of the neural network for the substraction of the peak base is described further in this article.
COBISS.SI-ID: 923818