Analytische Chemie
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A model is presented that employs a unified approach for
simulating the photon energy spectra for transmission and direct
beam targets composed of arbitrary homogeneous materials. In order
to achieve this, a detailed model of electron transport within the
target is employed. The validity of the developed model is shown
through comparisons with Monte Carlo simulations as well as
measurements for a number of different configurations.
The talk addresses the STANDARDIZATION OF ELLIPSOMETRY and the following points are discussed in more detail: historical background of ellipsometry, history of International Conferences on Ellipsometry, Workshops Ellipsometry in Germany and Europe, information on German/European Working Group Ellipsometry, technical/industrial importance of ellipsometry, applications on non-ideal material systems and standardization activities on ellipsometry.
Rail inspection performed by ultrasonic rail inspection trains is a complex and challenging process. A large number of variables and parameters given by the environment, the track and the testing-system have an influence on the overall performance of the inspection and the inspection result. Typically the parameter vary in a combination depending on the track condition.
To evaluate the individual influence of each relevant variable, simulation tools can be used. Therefore the entire inspection process has to be transferred into a model using combined modelling techniques.
The goal of this work is to model an instrumented ultrasonic test run with a rail inspection train with the parameters varied by a script over a virtually driven distance.
The main outcomes of the EMPIR project nPSize are presented and the suitability of the new capabilities (e.g. reference materials, measurement procedures, inter-laboratory comparisons) as pertinent contributions to normative projects within CEN/TC 352 Nanotechnologies are discussed. E.g. the first technical report of nPSize on full algorithm sequences for nanoparticle detection and size measurement as developed on both a physical basis (SEM, TSEM, AFM and SAXS) and by machine learning is put at disposal.