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Binary Neural Networks In article number http://doi.wiley.com/10.1002/aisy.202000134, Stephan Menzel and co‐workers explore a computation in‐memory concept for binary vector‐matrix multiplications based on complementary resistive switches. Experimental results on a small‐scale demonstrator are shown and the influence of device variability is investigated. The simulated inference of a 1‐layer fully connected binary neural network trained on the MNIST data set resulted in an accuracy of nearly 86%.

Nanostructured composite electrode materials play a major role in the field of catalysis and electrochemistry. Self‐assembly of metallic nanoparticles on oxide supports via metal exsolution relies on the transport of reducible dopants towards the perovskite surface to provide accessible catalytic centers at the solid/gas interface. However, it is unclear if exsolution can be driven from the oxide bulk or if the process is limited to surfaces and interfaces, where strong electrostatic gradients and space charges typically control the properties of oxides. Here we reveal that the nature of the surface‐dopant interaction is the main determining factor for the exsolution kinetics of nickel in SrTi0.9Nb0.05Ni0.05O3‐ẟ and that the exsolution depth is strongly limited to the near‐surface region of the perovskite oxide. Electrostatic interaction of dopants with surface space charge regions forming upon thermal annealing result in strong surface passivation i.e. a retarded exsolution response. We furthermore demonstrate the controllability of the exsolution response via engineering of the perovskite surface chemistry. Our findings indicate that tailoring the electrostatic gradients at the perovskite surface is an essential step to improve exsolution type materials in catalytic converters.

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Metal exsolution is a dynamic process that is driven under reducing atomosphere and at elevated temperatures, which results in the self-assembly of nanoparticles at the surface of complex perovskite catalysts. The nanoparticle characteristics of metal exsolution catalysts can be subject to considerable inhomogeneity, where the anisotropic surface properties of ceramic oxides were identified to have a major influence on the exsolution behavior. We systematically reveal the orientation-dependent anisotropy of the exsolution behavior of Ni in SrTi0.9Nb0.05Ni0.05O3-ẟ using multi-faceted epitaxial thin films, that represent a material system with properties in between functional ceramics and single-crystalline perovskite thin film model systems. Using an approach of combined orientation mapping and surface imaging we study the exsolution behavior with particular focus on the initial exsolution reponse i.e. after short annealing times. We find orientation-specific variations in the surface morphology of the thin film facets. In the as-prepared state, surface reconstructions cause the formation of patterned surface structures for all thin film facets apart from (001) surfaces, which exhibit a plain surface morphology as well as an enhanced exsolution response. Surface reconstructions and their inherent energy landscape may hence cause an additional energy barrier for the exsolution reaction that results in orientation-dependent differences in the exsolution kinetics.