Distribution of gamma-ray bursts on the t90-hardness ratio plane and their classification revisited

Using four mixed bivariate distributions (Normal distribution, Skew-Normal distribution, Student distribution, Skew-Student distribution) and bootstrap re-sampling analysis, we analyze the samples of CGRO/BATSE, Swift/BAT and Fermi/GBM gamma-ray bursts in detail on the t90-hardness ratio plane. The Bayesian information criterion is used to judge the goodness of fit for each sample, comprehensively. It is found that all the three samples show a symmetric (either normal or student) distribution. It is also found that the existence of three classes of gamma-ray bursts is preferred by the three samples, but the strength of this preference varies with the sample size: when the sample size of the data set is larger, the preference of three classes scheme becomes weaker. Therefore, the appearance of an intermediate class may be caused by a small sample size and the possibility that there are only two classes of gamma-ray bursts still cannot be expelled yet. A further bootstrap re-sampling analysis also confirms this result.Comment: 10 pages, 9 figure

2,2′-[1,1′-(Octane-1,8-diyldioxy­dinitrilo)diethyl­idyne]diphenol

The title compound, C24H32N2O4, has a crystallographic inversion centre at the mid-point of the central C—C bond. At each end of the mol­ecule, intra­molecular O—H⋯N hydrogen bonds generate six-membered S(6) ring motifs. The crystal structure is stabilized by pairs of weak inter­molecular C—H⋯O hydrogen bonds that link neighbouring mol­ecules into R 2 2(40) ring motifs, which in turn form infinite one-dimensional supra­molecular ribbon structures

Bis[(E)-4-bromo-2-(ethoxy­imino­meth­yl)phenolato-κ2 N,O 1]copper(II)

The title compound, [Cu(C9H9BrNO2)2], is a centrosymmetric mononuclear copper(II) complex. The Cu atom is four-coordinated in a trans-CuN2O2 square-planar geometry by two phenolate O and two oxime N atoms from two symmetry-related N,O-bidentate (E)-4-bromo-2-(ethoxy­imino­meth­yl)phenolate oxime-type ligands. An inter­esting feature of the crystal structure is the centrosymmetric inter­molecular Cu⋯O inter­action [3.382 (1) Å], which establishes an infinite chain structure along the b axis

Characterizing random-singlet state in two-dimensional frustrated quantum magnets and implications for the double perovskite Sr2_2CuTe1−x_{1-x}Wx_{x}O6_6

Motivated by experimental observation of the non-magnetic phase in the compounds with frustration and disorder, we study the ground state of the spin-$1/2$ square-lattice Heisenberg model with randomly distributed nearest-neighbor $J_1$ and next-nearest-neighbor $J_2$ couplings. By using the density matrix renormalization group (DMRG) calculation on cylinder system with circumference up to $10$ lattice sites, we identify a disordered phase between the N\'eel and stripe magnetic phase with growing $J_2 / J_1$ in the presence of strong randomness. The vanished spin-freezing parameter indicates the absent spin glass order. The large-scale DMRG results unveil the size-scaling behaviors of the spin-freezing parameter, the power-law decay of average spin correlation, and the exponential decay of typical spin correlation, which all agree with the corresponding behavior in the one-dimensional random singlet (RS) state and characterize the RS nature of this non-magnetic state. The DMRG simulation also opens new insight and opportunities for characterizing a class of non-magnetic states in two-dimensional frustrated magnets with disorder. We also compare with existing experiments and suggest more measurements for understanding the spin-liquid-like behavior in the double perovskite Sr$_2$CuTe$_{1-x}$W$_{x}$O$_6$.Comment: 11 pages,10 figure

Classifying superheavy elements by machine learning

Among the 118 elements listed in the periodic table, there are nine superheavy elements (Mt, Ds, Mc, Rg, Nh, Fl, Lv, Ts, and Og) that have not yet been well studied experimentally because of their limited half-lives and production rates. How to classify these elements for further study remains an open question. For superheavy elements, although relativistic quantum-mechanical calculations for the single atoms are more accurate and reliable than those for their molecules and crystals, there is no study reported to classify elements solely based on atomic properties. By using cutting-edge machine learning techniques, we find the relationship between atomic data and classification of elements, and further identify that Mt, Ds, Mc, Rg, Lv, Ts, and Og should be metals, while Nh and Fl should be metalloids. These findings not only highlight the significance of machine learning for superheavy atoms but also challenge the conventional belief that one can determine the characteristics of an element only by looking at its position in the table

4-Bromo-2-({4-[(hy­droxy­imino)­meth­yl]phen­yl}imino­meth­yl)phenol

In the title compound, C14H11BrN2O2, the mean planes of the two benzene rings are almost parallel to each other, making a dihedral angle of 4.09 (1)°. An intra­molecular O—H⋯N hydrogen bond occurs. In the crystal, inter­molecular O—H⋯N and C—H⋯O hydrogen bonds link the mol­ecules into a chain-like supra­molecular structure

2,2′-[Octane-1,8-diyldioxy­bis(nitrilo­methyl­idyne)]diphenol

The complete mol­ecule of the title compound, C22H28N2O4, is generated by a crystallographic inversion centre at the mid-point of the central C—C bond. The two benzene rings are parallel to each other with a perpendicular inter­planar spacing of 1.488 (2) Å. Intra­molecular O—H⋯N hydrogen bonds generate two six-membered rings with S(6) motifs. In the crystal, weak inter­molecular C—H⋯O hydrogen bonds link neighbouring mol­ecules into an infinite three-dimensional network, which is further stabilized by weak C—H⋯π inter­actions

Graphene-Based Nanostructures in Electrocatalytic Oxygen Reduction

Application of graphene-type materials in electrocatalysis is a topic of growing scientific and technological interest. A tremendous amount of research has been carried out in the field of oxygen electroreduction, particularly with respect to potential applications in the fuel cell research also with use of graphene-type catalytic components. This work addresses fundamental aspects and potential applications of graphene structures in the oxygen reduction electrocatalysis. Special attention will be paid to creation of catalytically active sites by using non-metallic heteroatoms as dopants, formation of hierarchical nanostructured electrocatalysts, their long-term stability, and application as supports for dispersed metals (activating interactions)