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Register a new userMonitoring ion track formation using in situ RBS/c and ERDA
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The aim of this work is to investigate feasibility of the ion beam analysis techniques for monitoring swift heavy ion track formation. First, use of the in situ Rutherford backscattering spectroscopy in channeling mode to observe damage build-up in quartz SiO2 after MeV heavy ion irradiation is demonstrated. Second, new results of the in situ grazing incidence time-of-flight elastic recoil detection analysis used for monitoring the surface elemental composition during ion tracks formation in various materials are presented. Ion tracks were found on SrTiO3, quartz SiO2, a-SiO2 and muscovite mica surfaces by atomic force microscopy, but in contrast to our previous studies on GaN and TiO2, surface stoichiometry remained unchanged.
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There is ongoing debate regarding the mechanism of swift heavy ion track formation in CaF2. The objective of this study is to shed light on this important topic using a range of complimentary experimental techniques. Evidence of the threshold for ion track formation being below 3 keV/nm is provided by both transmission electron microscopy and Rutherford backscattering spectroscopy in the channeling mode which has direct consequences for the validity of models describing the response of CaF2 to swift heavy ion irradiation. Advances in the TEM and RBS/c analyses presented here pave the way for better understanding of the ion track formation.
We report on the use of time-resolved optical ellipsometry to monitor the deposition of single atomic layers with subatomic sensitivity. Ruddlesden-Popper thin films of SrO(SrTiO3)n=4 were grown by means of metalorganic aerosol deposition in the atomic layer epitaxy mode on SrTiO3(100), LSAT(100) and DyScO3(110) substrates. The measured time dependences of ellipsometric angles, ${Delta}(t)$ and ${Psi}(t)$, were described by using a simple optical model, considering the sequence of atomic layers SrO and TiO2 with corresponding bulk refractive indices. As a result, valuable online information on the growth process, the film structure and defects were obtained. Ex situ characterization techniques, i.e. transmission electron microscopy (TEM), X-ray diffraction (XRD) and X- ray reflectometry (XRR) verify the crystal structure and confirm the predictions of optical ellipsometry.
Complex oxide perovskites have been widely studied for their diverse functional properties. When dimensionally reduced to epitaxial thin films and heterostructures these properties are frequently tunable, and the symmetry-breaking inherent to thin film structures can result in the emergence of new, novel, phenomena and properties. However, the ability to control and harness these structures relies on an atomic-level understanding and control of the growth process, made challenging by the lack of suitable in situ compositional characterization tools. In this work, the compositional-dependence of SrTiO3 on pulsed laser deposition growth parameters is investigated with in situ Auger electron spectroscopy and ex situ thin film x-ray diffraction, and verified with a simple escape depth model. We show that this is a suitable technique for monitoring subtle compositional shifts occurring during the deposition process, with broad implications for the continued development of thin film synthesis techniques.
The dissociative chemisorption of molecular nitrogen on clean lanthanide surfaces at ambient temperature and low pressure is explored. In-situ conductance measurements track the conversion from the lanthanide metals to the insulating lanthanide nitrides. A small partial pressure of oxygen ($sim 10^{-8}$ mbar) is shown to inhibit the nitridation of lanthanides at $10^{-4}$ mbar of N$_2$. The rate of nitridation as a function of nitrogen pressure is measured at low pressure for a series of lanthanide elements, gadolinium, terbium, dysprosium, ytterbium and praseodymium. Exposure of the lanthanide surfaces to both N$_2$ and H$_2$ results in the formation of NH$_3$.
Computer vision based methods have been explored in the past for detection of railway track defects, but full automation has always been a challenge because both traditional image processing methods and deep learning classifiers trained from scratch fail to generalize that well to infinite novel scenarios seen in the real world, given limited amount of labeled data. Advancements have been made recently to make machine learning models utilize knowledge from a different but related domain. In this paper, we show that even though similar domain data is not available, transfer learning provides the model understanding of other real world objects and enables training production scale deep learning classifiers for uncontrolled real world data. Our models efficiently detect both track defects like sunkinks, loose ballast and railway assets like switches and signals. Models were validated with hours of track videos recorded in different continents resulting in different weather conditions, different ambience and surroundings. A track health index concept has also been proposed to monitor complete rail network.
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