DECHEMA

About: DECHEMA is a nonprofit organization based out in Frankfurt am Main, Germany. It is known for research contribution in the topics: Corrosion & Oxide. The organization has 756 authors who have published 1307 publications receiving 25693 citations.

Protection of γ-based TiAl against high temperature oxidation using ion implantation of chlorine

Abstract: The effect of ion beam implantation and plasma immersion ion implantation of chlorine on the high temperature oxidation of titanium aluminides above 800°C in air was investigated. Thermogravimetric oxidation tests (TGA) were performed to examine the long term protection. Depth profiling with Auger electron spectroscopy (AES) was used to investigate Cl diffusion and oxide formation during the first stage of oxidation. A microscopic model of the ‘Cl-effect’ will be discussed. A systematic variation of the implantation energy and fluence shows that there is a narrow regime of Cl concentration for optimum protective effect. The time to form a protective Al 2 O 3 layer depends on the local Cl concentration. The oxidation rate after this incubation time is reduced by about two orders of magnitude compared to untreated Ti50Al and is nearly independent of the fluence. The implantation energy is not a sensitive parameter because the implanted chlorine profile changes very quickly during high temperature oxidation.

Scale formation on γ-TiAl during oxidation at 800 and 900°C in air and in He + 20% O2

Abstract: The oxide scale formation on γ-TiAl at 800 and 900°C was studied using high temperature X-ray diffraction as anin situ-method. The experiments were performed in air and in He with 20 vol.% O2. The formation of alumina in the form of α-Al2O3 and of TiO2 in the form of rutile was observed in both atmospheres and the formation of TiN was detected in air. Depending on the atmosphere the diffraction peaks of two different additional phases were detected, which do not exist in any data base nor in the Ti-Al-O phase diagram. One of them, the Z-phase, appears in He with 20 vol.% O2 and the other, the X-phase, in air. The Zphase was also found at room temperature after oxidation at 900°C in air. The growth of both phases, X and Z, starts immediately with the oxidation process and follows the parabolic rate law.

Nomenclature, symbols and definitions in electrochemical engineering (IUPAC Recommendations 1993)

Abstract: This document treats performance parameters in electrochemical engineering. Nomenclature and definitions for electrochemical engineering together with the respective symbols are given. Parameters to account for electrochemical reactions in electrolysis as well as for electrochemical power sources (electrochemical energy generators) are included in this document.

Polypyrrole as the Active Material for Potentiometric Sensors

Abstract: In this study two types of sensor applications using polypyrrole (PPy) as the active material will be considered. The fisrt part deals with the development of a new type of pH glass electrode, where PPy is used as a mixed electron/ion conductor, to replace the conventional liquid junction reference electrode in a buffer electrolyte solution by an all-solid-state contact interface. The second part deals with PPy as an ion-sensitive material for the development of a potentiometric sensor, e.g. for the measurement of the pH or the detection of iron in aqueous solutions.

Dynamic Modeling of Chemical Reaction Systems with Neural Networks and Hybrid Models

Abstract: A common problem in kinetic modeling of complex chemical reactions is that a rigorous description of the reaction system, e.g., based on elementary reactions, is not possible. This is because either the reaction involves too many reactions and intermediates or the reaction mechanism is not known in sufficient detail. Alternative data-driven modeling, e.g., using neural networks, normally demands large amounts of experimental data and has poor generalization capability. In such situations a combined physical and data-driven (i.e. hybrid) model may be attractive, that utilizes the specific advantages of both approaches while avoiding their disadvantages. This paper explains the procedure of hybrid modeling of integral (i.e. time-dependent) data by using examples from chemical kinetics. The benefits of the hybrid models are described in comparison to the limiting cases of purely physical and purely data-driven models. In general, the hybrid model surpasses the purely physical and neural network models in terms of a combined interpolation- and extrapolation-range criterion.