Feasibility Study on the Generation of Nanoporous Metal Structures by Means of Selective Alloy Depletion in Halogen-Rich Atmospheres.

A new approach to produce nanoporous metals has been investigated, which is based on the dealloying of bi- or multi-component alloys. Depletion and pore formation of the alloy substrate are obtained by the transport of certain alloy components at high temperatures via volatile halogen compounds. These halogen compounds are transferred to materials acting as sinks based on their higher affinity to the respective components, and chemically bound there. Transfer via volatile halogen compounds is known from the pack cementation coating process and from high-temperature corrosion in certain industrial atmospheres. The approach was tested on different precursor alloys: Ti-43.5Al-4Nb-1Mo-0.1B (TNM-B1), TiNb42, and AlCu. Both dealloying effects and micro-scale pore formation were observed. The detailed size of the porous structures is in the range of 50 nm for both TNM-B1 and TiNB42 and 500 nm for AlCu.

Microstructure and Deformation Response of TRIP-Steel Syntactic Foams to Quasi-Static and Dynamic Compressive Loads.

The implementation of hollow S60HS glass microspheres and Fillite 106 cenospheres in a martensitically transformable AISI 304L stainless steel matrix was realized by means of metal injection molding of feedstock with varying fractions of the filler material. The so-called TRIP-steel syntactic foams were studied with respect to their behavior under quasi-static compression and dynamic impact loading. The interplay between matrix material behavior and foam structure was discussed in relation to the findings of micro-structural investigations, electron back scatter diffraction EBSD phase analyses and magnetic measurements. During processing, the cenospheres remained relatively stable retaining their shape while the glass microspheres underwent disintegration associated with the formation of pre-cracked irregular inclusions. Consequently, the AISI 304L/Fillite 106 syntactic foams exhibited a higher compression stress level and energy absorption capability as compared to the S60HS-containing variants. The α ′ -martensite kinetic of the steel matrix was significantly influenced by material composition, strain rate and arising deformation temperature. The highest ferromagnetic α ′ -martensite phase fraction was detected for the AISI 304L/S60HS batches and the lowest for the TRIP-steel bulk material. Quasi-adiabatic sample heating, a gradual decrease in strain rate and an enhanced degree of damage controlled the mechanical deformation response of the studied syntactic foams under dynamic impact loading.

Collaborative decision support and documentation in chemical safety with KnowSEC.

To protect the health of human and environment, the European Union implemented the REACH regulation for chemical substances. REACH is an acronym for Registration, Evaluation, Authorization, and Restriction of Chemicals. Under REACH, the authorities have the task of assessing chemical substances, especially those that might pose a risk to human health or environment. The work under REACH is scientifically, technically and procedurally a complex and knowledge-intensive task that is jointly performed by the European Chemicals Agency and member state authorities in Europe. The assessment of substances under REACH conducted in the German Environment Agency is supported by the knowledge-based system KnowSEC, which is used for the screening, documentation, and decision support when working on chemical substances. The software KnowSEC integrates advanced semantic technologies and strong problem solving methods. It allows for the collaborative work on substances in the context of the European REACH regulation. We discuss the applied methods and process models and we report on experiences with the implementation and use of the system.

Semi-automatic learning of simple diagnostic scores utilizing complexity measures.

OBJECTIVE: Knowledge acquisition and maintenance in medical domains with a large application domain ontology is a difficult task. To reduce knowledge elicitation costs, semi-automatic learning methods can be used to support the domain specialists. They are usually not only interested in the accuracy of the learned knowledge: the understandability and interpretability of the learned models is of prime importance as well. Then, often simple models are more favorable than complex ones. METHODS AND MATERIAL: We propose diagnostic scores as a promising approach for the representation of simple diagnostic knowledge, and present a method for inductive learning of diagnostic scores. It can be incrementally refined by including background knowledge. We present complexity measures for determining the complexity of the learned scores. RESULTS: We give an evaluation of the presented approach using a case base from the fielded system SonoConsult. We further discuss that the user can easily balance between accuracy and complexity of the learned knowledge applying the presented measures. CONCLUSIONS: We argue that semi-automatic learning methods can support the domain specialist efficiently when building (diagnostic) knowledge systems from scratch. The presented complexity measures allow for an intuitive assessment of the learned patterns.