Crystal-Structure Simulation of Methylthiolated Peri-Condensed Polycyclic Aromatic Hydrocarbons for Identifying Promising Molecular Semiconductors: Discovery of 1,3,8,10-tetrakis(methylthio)peropyrene Showing Ultrahigh Mobility.

The crystal structures of molecular semiconductors critically affect their carrier-transport properties. One of the promising crystal structures that afford high carrier mobility is a brickwork structure recently reported for 1,3,6,8-tetrakis(methylthio)pyrene (MT-pyrene) showing ultrahigh mobility. However, such ultrahigh mobility is not realized in other methylchalocogenolated pyrenes, owing to subtle differences in the molecular positions in their crystal structures. This means that, for developing superior molecular semiconductors, it is desirable to simulate the crystal structure with sufficient quality before time-consuming and labor-intensive synthetic trials. To realize this, a new computational approach is developed to simulate crystal structures of all methylchalocogenolated pyrenes, which is then applied to MT-pyrene-related methylthiolated peri-condensed polycyclic aromatic hydrocarbons including perylene, peropyrene, and terrylene derivatives. Among these, 1,3,8,10-tetrakis(methylthio)peropyrene (MT-peropyrene) is expected to show high mobility based on the simulated crystal structures. Thus, MT-peropyrene is chosen as the synthetic target, and a new peropyrene synthesis method is developed. Thus synthesized MT-peropyrene has virtually the same crystal structure as the simulated one, and its single-crystal field-effect transistors show mobility as high as 30 cm2 V-1 s-1 and band-like transport behaviors. These results indicate that the present crystal-structure simulation is a powerful tool for exploring promising molecular semiconductors.

"Manipulation" of Crystal Structure by Methylthiolation Enabling Ultrahigh Mobility in a Pyrene-Based Molecular Semiconductor.

Control and prediction of crystal structures of molecular semiconductors are considered challenging, yet they are crucial for rational design of superior molecular semiconductors. It is here reported that through methylthiolation, one can rationally control the crystal structure of pyrene derivatives as molecular semiconductors; 1,6-bis(methylthio)pyrene keeps a similar sandwich herringbone structure to that of parent pyrene, whereas 1,3,6,8-tetrakis(methylthio)pyrene (MT-pyrene) takes a new type of brickwork structure. Such changes in these crystal structures are explained by the alteration of intermolecular interactions that are efficiently controlled by methylthiolation. Single crystals of MT-pyrene are evaluated as the active semiconducting material in single-crystal field-effect transistors (SC-FETs), which show extremely high mobility (32 cm2 V-1 s-1 on average) operating at the drain and gate voltages of -5 V. Moreover, the band-like transport and very low trap density are experimentally confirmed for the MT-pyrene SC-FETs, testifying that the MT-pyrene is among the best molecular semiconductors for the SC-FET devices.

Bicyclic imidazoles for modular synthesis of chiral imidazolium salts.

The preparation of new chiral bicyclic imidazoles 5 and their transformation into imidazolium salts 6 and 7 are reported. The utility of the salts as precursors for chiral N-heterocyclic carbenes was demonstrated by the synthesis of C-N-C pincer Ni-complex 13h, the structure of which was characterized by single-crystal X-ray analysis.

Synthesis of 3-hydroxypyridines using ruthenium-catalyzed ring-closing olefin metathesis.

New synthetic routes to substituted 3-hydroxypyridines 6 are presented. Ring-closing olefin metathesis (RCM)/elimination and RCM/oxidation/deprotection of nitrogen-containing dienes 4 are the key processes of the routes. An application of RCM/oxidation/deprotection to the synthesis of 3-aminopyridine 13f is also described.

A maximum likelihood approach to density estimation with semidefinite programming.

Density estimation plays an important and fundamental role in pattern recognition, machine learning, and statistics. In this article, we develop a parametric approach to univariate (or low-dimensional) density estimation based on semidefinite programming (SDP). Our density model is expressed as the product of a nonnegative polynomial and a base density such as normal distribution, exponential distribution, and uniform distribution. When the base density is specified, the maximum likelihood estimation of the polynomial is formulated as a variant of SDP that is solved in polynomial time with the interior point methods. Since the base density typically contains just one or two parameters, computation of the maximum likelihood estimate reduces to a one- or two-dimensional easy optimization problem with this use of SDP. Thus, the rigorous maximum likelihood estimate can be computed in our approach. Furthermore, such conditions as symmetry and unimodality of the density function can be easily handled within this framework. AIC is used to choose the best model. Through applications to several instances, we demonstrate flexibility of the model and performance of the proposed procedure. Combination with a mixture approach is also presented. The proposed approach has possible other applications beyond density estimation. This point is clarified through an application to the maximum likelihood estimation of the intensity function of a nonstationary Poisson process.