2,899 research outputs found
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 SrCuTeWO
Motivated by experimental observation of the non-magnetic phase in the
compounds with frustration and disorder, we study the ground state of the
spin- square-lattice Heisenberg model with randomly distributed
nearest-neighbor and next-nearest-neighbor couplings. By using the
density matrix renormalization group (DMRG) calculation on cylinder system with
circumference up to lattice sites, we identify a disordered phase between
the N\'eel and stripe magnetic phase with growing 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
SrCuTeWO.Comment: 11 pages,10 figure
THREE HAMP GENES OF NILE TILAPIA WERE DIFFERENTIALLY ACTIVATED IN SPLEEN TO DEFEND AGAINST STREPTOCOCCUS INIAE INFECTION
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)
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