The asymmetric cyanation of acylsilanes affords densely functionalized tetrasubstituted chiral carbon centers bearing silyl, cyano, and hydroxy groups, which are of particular interest in synthetic and medicinal chemistry. However, this method has been limited to a few enzymatic approaches, which employ only one substrate because of substrate specificity. Here we show the non-enzymatic catalytic asymmetric cyanation of acylsilanes using a chiral Lewis base as an enantioselective catalyst, trimethylsilyl cyanide as a cyanating reagent, and isopropyl alcohol as an additive to drive catalyst turnover. High enantio- and site-selectivities are achieved in a catalytic manner, and a variety of functional groups are installed in optically active acylsilane cyanohydrins, thus overcoming the limitations imposed by substrate specificity in conventional enzymatic methods. A handle for the synthetic application of the products is also established through the development of a catalyst for protecting acylsilane cyanohydrins, which are unstable and difficult to protect alcohols.

The arm-driven inverted pendulum is an unstable and nonlinear system. Therefore, the feedback controller designed by the linear control theory cannot be stabilized in the wide range. This paper discusses a nonlinear control design based on the optimal regulator problem. However, it is difficult to get the analysis solution for this problem. In this paper, DE (Differential Evolution) and PSO (Particle Swarm Optimization) methods are used to get an approximate solution. In addition to the first order feedback gain designed by the linear optimal regulator, we design the higher order gain. Finally, the validity of our nonlinear controller designed by DE is verified through some experiments.

A process optimization method based on partial least squares (PLS) has been used in pharmaceutical processes. However, its applicability and performance are limited because PLS cannot cope with nonlinearity and changes in process characteristics. In this research, a new process optimization method based on locally weighted PLS (LW-PLS) is proposed. To solve a nonlinear optimization problem based on LW-PLS, in which any global model is not constructed, self-adaptive differential evolution (jDE) is adopted. The validity of the proposed method is demonstrated through a numerical example and an industrial case study of a pharmaceutical granulation process.

No abstract