Enantioselective Synthesis of α‐Secondary and α‐Tertiary Piperazin‐2‐ones and Piperazines by Catalytic Asymmetric Allylic Alkylation

The asymmetric palladium‐catalyzed decarboxylative allylic alkylation of differentially N‐protected piperazin‐2‐ones allows the synthesis of a variety of highly enantioenriched tertiary piperazine‐2‐ones. Deprotection and reduction affords the corresponding tertiary piperazines, which can be employed for the synthesis of medicinally important analogues. The introduction of these chiral tertiary piperazines resulted in imatinib analogues which exhibited comparable antiproliferative activity to that of their corresponding imatinib counterparts.     

Synthesis of Indolines and Derivatives by Aza‐Heck Cyclization

For the first time, an aza‐Heck cyclization that allows the preparation of indoline scaffolds is described. Using N‐hydroxy anilines as electrophiles, which can be easily accessed from the corresponding nitroarenes, this method provides indolines bearing pendant functionality and complex ring topologies. Synthesis of challenging indolines, such as those bearing fully substituted carbon atoms at C2, is also possible using this method.     

An efficient time-step-based self-adaptive algorithm for predictor-corrector methods of Runge-Kutta type

Finding an efficient implementation variant for the numerical solution of problems from computational science and engineering involves many implementation decisions that are strongly influenced by the specific hardware architecture. The complexity of these architectures makes it difficult to find the best implementation variant by manual tuning. For numerical solution methods from linear algebra, auto-tuning techniques based on a global search engine as they are used for ATLAS or FFTW can be used successfully. These techniques generate different implementation variants at installation time and select one of these implementation variants either at installation time or at runtime, before the computation starts. For some numerical methods, auto-tuning at installation time cannot be applied directly, since the best implementation variant may strongly depend on the specific numerical problem to be solved. An example is solution methods for initial value problems (IVPs) of ordinary differential equations (ODEs), where the coupling structure of the ODE system to be solved has a large influence on the efficient use of the memory hierarchy of the hardware architecture. In this context, it is important to use auto-tuning techniques at runtime, which is possible because of the time-stepping nature of ODE solvers.In this article, we present a sequential self-adaptive ODE solver that selects the best implementation variant from a candidate pool at runtime during the first time steps, i.e., the auto-tuning phase already contributes to the progress of the computation. The implementation variants differ in the loop structure and the data structures used to realize the numerical algorithm, a predictor-corrector (PC) iteration scheme with Runge-Kutta (RK) corrector considered here as an example. For those implementation variants in the candidate pool that use loop tiling to exploit the memory hierarchy of a given hardware platform we investigate the selection of tile sizes. The self-adaptive ODE solver combines empirical search with a model-based approach in order to reduce the search space of possible tile sizes. Runtime experiments demonstrate the efficiency of the self-adaptive solver for different IVPs across a range of problem sizes and on different hardware architectures.     

Magnetic Nanoparticles Obtained by Electrophysical Technique: Focus on Biomedical Applications

Physics of the Solid State - The possibilities of producing large batches of magnetic nanoparticles (MNPs) using electrical explocion of the wire (EEW), laser target evaporation, and spark...