The gas phase hydrogenation of acrolein over silver has been studied in a broad pressure range from ~2 mbar to 20 bar and with various silver materials (single crystals, sputtered silver, silica supported Ag nanoparticles) in an attempt to examine the question of "pressure and materials gap" in catalysis. High pressures as well as nanoparticles favour the formation of allyl alcohol (selectivities up to 42 %), whereas with the opposite conditions propionaldehyde is by far the main product. A critical minimum reaction pressure was identified: below ca. 100 mbar no allyl alcohol was formed. In situ-XAS measurements have been performed at 7.5 mbar in order to gain insight into the interaction of acrolein with silver samples. Despite the fact that beam-induced processes have been observed, it is concluded that at low pressures, acrolein orientates parallel to the surface on Ag (111) and is present at the surface in the form of hydrogenated propionaldehyde-like species. The influence of catalyst structure and pressure on the adsorption geometry of acrolein as well as the possible rate-determining step in acrolein hydrogenation are discussed.

The hydrogenation of p-nitrotoluol to p-toluidin was done both in a fixed bed reactor and in a microchannel reactor made of a stack of micro structured aluminum wafers. Two procedures for the deposition of the catalytic active component palladium on the microstructured aluminum wafers are described. The hydrogenation results are discussed. Advantages and drawbacks of both reactor types are shown.

The gas phase hydrogenation of acrolein over 7.5% Ag/SiO 2 has been studied in a broad pressure range from 7.5 mbar to 20 bar. Higher pressures favour the formation of allyl alcohol (selectivities up to 42 %), whereas at low pressures propionaldehyde is by far the main product. In situ-XAS has been performed at 7.5 mbar in order to gain insight into the interaction of acrolein with Ag(111). Hydrogenated propionaldehyde-like surface species could be detected which orientated parallel to the surface. The observed intermediate correlates perfectly with the online catalytic data.

The main objective of High-Throughput Experimentation (HTE) in catalysis and materials discovery, as well as in other areas, is to increase the total experiment count for a given time interval either by speeding up individual experiments and/or by running experiments in parallel. Typically, such screening experiments involve the preparation of sample libraries consisting of a large number of diverse materials together with the extensive variation of conditions during performance tests within a wide parameter space. As an important difference, when compared to molecular high-throughput screening, materials science does not always deal with uniquely defined entities. Every parameter during preparation and testing may be a factor crucial for the performance of the material. As a consequence, all experimental parameters should be controlled or at least recorded to be able to identify important correlations. The prime goal of the HTE cycle lies in speeding up the whole discovery and optimization process, but minimizing the costs and human efforts needed in the experimental workflow. In short, an increase in productivity will always result in a faster knowledge gain and therefore be a competitive advantage. This goal is only to be achieved by utilizing software tools at every single stage of the HTE process. In addition, the software platform has to provide an interface for data-mining/feature-extraction tools to gain insights required for discovering new useful materials. The software requirements in the HTE area can be classified according to the following aspects: "support and tracking of material preparation (workflow management)", "planning and setting up experiments/performance tests", "process automation and control of hardware devices", "data logging and post-processing", "data storage, management and analysis". Herein we present a modular approach to an HTE software platform. Instead of a monolithic master system, small tools with a limited set of tasks are interconnected using standardized, self-descriptive data structures. This approach is highly flexible with respect to the rapidly changing needs of the chemists: Since the modules are isolated and inter-module communication is standardized, new components (e.g., new devices) can be integrated into the process without any side effects.

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