In order to develop an efficient and greener method for organic chemical hydride production in the energy carrier system, the electrocatalytic hydrogenation of toluene to methylcyclohexane was carried out using a proton exchange membrane (PEM) reactor, which generally applied a polymer electrolyte fuel cell and industrial electrolysis technologies. The electrochemical conversion proceeded in high current efficiencies (>90%) under sufficiently mild conditions with various metal-supported catalysts such as Pt/C, Rh/C, Ru/C, and PtRu/C. For example, methylcyclohexane was obtained in 94% current efficiency by the electrochemical hydrogenation of toluene with PtRu/C. Although the current efficiency of the hydrogenation with Pt/C, Rh/C, and Ru/C apparently decreased under high current density conditions due to the side reaction (hydrogen evolution), the hydrogenation with PtRu/C catalysts proceeded in excellent efficiencies even under high current density conditions.
We have investigated the electrochemical hydrogenation of toluene using a PEM reactor for development of an organic chemical hydride system. Especially, the influence of catalyst materials such as Pt, Ru, and PtRu for a PEM reactor on the by-product formation and product selectivity in the hydrogenation of toluene was investigated.
An indium-catalyzed reaction of lactones and a disilathiane leading to thiolactones is described. The direct synthesis of thiolactones from lactones with an appropriate sulfur source is one of the most attractive approaches in organic and pharmaceutical chemistry. In this context, we found an indium-catalyzed direct conversion of lactones into thiolactones in the presence of elemental sulfur and a hydrosilane via formation of the disilathiane in situ. On the basis of the previous reaction, the application utilizing the disilathiane as a sulfur source was performed herein for the efficient synthesis of a variety of thiolactone derivatives from lactones by an indium catalyst.
This study considers the common situation in data analysis when there are few observations of the distribution of interest or the target distribution, while abundant observations are available from auxiliary distributions. In this situation, it is natural to compensate for the lack of data from the target distribution by using data sets from these auxiliary distributions-in other words, approximating the target distribution in a subspace spanned by a set of auxiliary distributions. Mixture modeling is one of the simplest ways to integrate information from the target and auxiliary distributions in order to express the target distribution as accurately as possible. There are two typical mixtures in the context of information geometry: the [Formula: see text]- and [Formula: see text]-mixtures. The [Formula: see text]-mixture is applied in a variety of research fields because of the presence of the well-known expectation-maximazation algorithm for parameter estimation, whereas the [Formula: see text]-mixture is rarely used because of its difficulty of estimation, particularly for nonparametric models. The [Formula: see text]-mixture, however, is a well-tempered distribution that satisfies the principle of maximum entropy. To model a target distribution with scarce observations accurately, this letter proposes a novel framework for a nonparametric modeling of the [Formula: see text]-mixture and a geometrically inspired estimation algorithm. As numerical examples of the proposed framework, a transfer learning setup is considered. The experimental results show that this framework works well for three types of synthetic data sets, as well as an EEG real-world data set.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.