Orbital Configurations and Magnetic Properties of Double-Layered Antiferromagnet Cs3_3Cu2_2Cl4_4Br3_3

We report the single-crystal X-ray analysis and magnetic properties of a new double-layered perovskite antiferromagnet, Cs$_3$Cu$_2$Cl$_4$Br$_3$. This structure is composed of Cu$_2$Cl$_4$Br$_3$ double layers with elongated CuCl$_4$Br$_2$ octahedra and is closely related to the Sr$_3$Ti$_2$O$_7$ structure. An as-grown crystal has a singlet ground state with a large excitation gap of $\Delta/k_{\rm B}\simeq 2000$ K, due to the strong antiferromagnetic interaction between the two layers. Cs$_3$Cu$_2$Cl$_4$Br$_3$ undergoes a structural phase transition at $T_{\rm s}\simeq330$ K accompanied by changes in the orbital configurations of Cu$^{2+}$ ions. Once a Cs$_3$Cu$_2$Cl$_4$Br$_3$ crystal is heated above $T_{\rm s}$, its magnetic susceptibility obeys the Curie-Weiss law with decreasing temperature even below $T_{\rm s}$ and does not exhibit anomalies at $T_{\rm s}$. This implies that in the heated crystal, the orbital state of the high-temperature phase remains unchanged below $T_{\rm s}$, and thus, this orbital state is the metastable state. The structural phase transition at $T_{\rm s}$ is characterized as an order-disorder transition of Cu$^{2+}$ orbitals.Comment: 6pages. 6figures, to appear in J. Phys. Soc. Jpn. Vol.76 No.

Spin Driven Jahn-Teller Distortion in a Pyrochlore system

The ground-state properties of the spin-1 antiferromagnetic Heisenberg model on the corner-sharing tetrahedra, pyrochlore lattice, is investigated. By breaking up each spin into a pair of 1/2-spins, the problem is reduced to the equivalent one of the spin-1/2 tetrahedral network in analogy with the valence bond solid state in one dimension. The twofold degeneracy of the spin-singlets of a tetrahedron is lifted by a Jahn-Teller mechanism, leading to a cubic to tetragonal structural transition. It is proposed that the present mechanism is responsible for the phase transition observed in the spin-1 spinel compounds ZnV$_2$O$_4$ and MgV$_2$O$_4$.Comment: 4 pages, 3 eps figures, REVTeX, to appear in Phys. Rev. Let

Breaking chirality in nonequilibrium systems on the lattice

We study the dynamics of fronts in parametrically forced oscillating lattices. Using as a prototypical example the discrete Ginzburg-Landau equation, we show that much information about front bifurcations can be extracted by projecting onto a cylindrical phase space. Starting from a normal form that describes the nonequilibrium Ising-Bloch bifurcation in the continuum and using symmetry arguments, we derive a simple dynamical system that captures the dynamics of fronts in the lattice. We can expect our approach to be extended to other pattern-forming problems on lattices

Reconstruction of incomplete X ray diffraction pole figures of oligocrystalline materials using deep learning

X ray diffraction crystallography allows non destructive examination of crystal structures. Furthermore, it has low requirements regarding surface preparation, especially compared to electron backscatter diffraction. However, up to now, X ray diffraction has been highly time consuming in standard laboratory conditions since intensities on multiple lattice planes have to be recorded by rotating and tilting. Furthermore, examining oligocrystalline materials is challenging due to the limited number of diffraction spots. Moreover, commonly used evaluation methods for crystallographic orientation analysis need multiple lattice planes for a reliable pole figure reconstruction. In this article, we propose a deep learning based method for oligocrystalline specimens, i.e., specimens with up to three grains of arbitrary crystal orientations. Our approach allows faster experimentation due to accurate reconstructions of pole figure regions, which we did not probe experimentally. In contrast to other methods, the pole figure is reconstructed based on only a single incomplete pole figure. To speed up the development of our proposed method and for usage in other machine learning algorithms, we introduce a GPU based simulation for data generation. Furthermore, we present a pole widths standardization technique using a custom deep learning architecture that makes algorithms more robust against influences from the experiment setup and materia

An Integrated Framework to Study Ecological Tipping Points in Social-Ecological Systems

Sudden regime shifts or tipping points pose a major threat to various ecosystems and people\u27s livelihoods worldwide. However, tipping points are still hard to predict and often occur without warning. To avoid dramatic social-ecological consequences, it is crucial to understand tipping point behaviour and to identify early warning indicators. Previous studies have hardly implemented an integrated social-ecological approach, which has led to a fragmented understanding and oversimplification of tipping point phenomena. Against this background, we present a systemic research framework that harmonizes ecological and social perspectives to gain a mechanistic understanding of tipping point behaviour. We utilize a social-ecological systems (SES) approach to identify drivers, consequences, and feasible preventive strategies. Our proposed framework consists of a retrospective, a comparative and a prospective perspective; each of them utilizes interdisciplinary studies in both sub systems at multiple scales. The research framework was developed by the members of NamTip, an inter- and transdisciplinary research project aiming to understand and manage desertification tipping points in Namibia’s semi-arid rangelands. The NamTip project represents a practical implementation of the research framework, that uses an integrated, social-ecological study design combining the threefold approach with dynamic modelling. This includes analyses of time-series and archival data, experimental and observational studies, as well as scenario development and exploration of decision-making with local farmers. After the initial practical implementation and with our ongoing evaluation, we are convinced that such an ambitious and complex framework will guide the way to a profound understanding of tipping point phenomena and feasible management options

Aneuploidy and Confined Chromosomal Mosaicism in the Developing Human Brain

BACKGROUND: Understanding the mechanisms underlying generation of neuronal variability and complexity remains the central challenge for neuroscience. Structural variation in the neuronal genome is likely to be one important mechanism for neuronal diversity and brain diseases. Large-scale genomic variations due to loss or gain of whole chromosomes (aneuploidy) have been described in cells of the normal and diseased human brain, which are generated from neural stem cells during intrauterine period of life. However, the incidence of aneuploidy in the developing human brain and its impact on the brain development and function are obscure. METHODOLOGY/PRINCIPAL FINDINGS: To address genomic variation during development we surveyed aneuploidy/polyploidy in the human fetal tissues by advanced molecular-cytogenetic techniques at the single-cell level. Here we show that the human developing brain has mosaic nature, being composed of euploid and aneuploid neural cells. Studying over 600,000 neural cells, we have determined the average aneuploidy frequency as 1.25-1.45% per chromosome, with the overall percentage of aneuploidy tending to approach 30-35%. Furthermore, we found that mosaic aneuploidy can be exclusively confined to the brain. CONCLUSIONS/SIGNIFICANCE: Our data indicates aneuploidization to be an additional pathological mechanism for neuronal genome diversification. These findings highlight the involvement of aneuploidy in the human brain development and suggest an unexpected link between developmental chromosomal instability, intercellural/intertissular genome diversity and human brain diseases