Understanding amorphous phase-change materials from the viewpoint of Maxwell rigidity

Phase-change materials (PCMs) are the subject of considerable interest because they have been recognized as potential active layers for next-generation non-volatile memory devices, known as Phase Change Random Access Memories (PRAMs). By analyzing First Principles Molecular Dynamics simulations we develop a new method for the enumeration of mechanical constraints in the amorphous phase and show that the phase diagram of the most popular system (Ge-Sb-Te) can be split into two compositional regions having a well-defined mechanical character: a Tellurium rich flexible phase, and a stressed rigid phase that encompasses the known PCMs. This sound atomic scale insight should open new avenues for the understanding of PCMs and other complex amorphous materials from the viewpoint of rigidity.Comment: 5 pages, 4 figures in EP

Electronic and Sturctural Transitions in Dense Liquid Sodium

At ambient conditions, the light alkali metals are free-electron like crystals with a highly symmetric structure. However, they were shown recently to exhibit unexpected complexity under pressure. It was predicted from theory and later confirmed by experiment that Li and Na undergo a sequence of symmetry breaking transitions driven by a Peierls mechanism. Most recently, measurements of the Na melting curve revealed an unprecedented and still unexplained drop in the melting temperature from 1000 K at 30 GPa to room temperature at 120 GPa. Here we report results from ab initio calculations that explain the unusual melting behavior in dense Na. We show that molten Na undergoes a series of pressure-induced structural and electronic transitions analogous to that observed in solid Na, but commencing at much lower pressure in the presence of disorder. With increasing pressure, liquid Na initially evolves by assuming a more compact local structure. However, a transition to a lower coordinated liquid takes place at a pressure around 65 GPa, accompanied by a threefold drop in electrical conductivity. A pseudogap opening at the Fermi level, an effect previously not observed in a liquid metal, drives this transition. Remarkably, the lower coordinated liquid emerges at rather elevated temperatures and above the stability region of a closed packed free electron-like metal. We predict that similar exotic behavior is possible in other materials as well

Discovering electron transfer driven changes in chemical bonding in lead chalcogenides (PbX, where X = Te, Se, S, O)

Understanding the nature of chemical bonding in solids is crucial to comprehend the physical and chemical properties of a given compound. To explore changes in chemical bonding in lead chalcogenides (PbX, where X = Te, Se, S, O), a combination of property-, bond breaking- and quantum-mechanical bonding descriptors have been applied. The outcome of our explorations reveals an electron transfer driven transition from metavalent bonding in PbX (X = Te, Se, S) to iono-covalent bonding in beta-PbO. Metavalent bonding is characterized by adjacent atoms being held together by sharing about a single electron and small electron transfer (ET). The transition from metavalent to iono-covalent bonding manifests itself in clear changes in these quantum-mechanical descriptors (ES and ET), as well as in property-based descriptors (i.e. Born effective charge, dielectric function, effective coordination number (ECON) and mode-specific Grueneisen parameter, and in bond breaking descriptors (PME). Metavalent bonding collapses, if significant charge localization occurs at the ion cores (ET) and/or in the interatomic region (ES). Predominantly changing the degree of electron transfer opens possibilities to tailor materials properties such as the chemical bond and electronic polarizability, optical band gap and optical interband transitions characterized by the imaginary part of the dielectric function. Hence, the insights gained from this study highlight the technological relevance of the concept of metavalent bonding and its potential for materials design

First-principles study of the effect of charge on the stability of a diamond nanocluster surface

Effects of net charge on the stability of the diamond nanocluster are investigated using the first-principles pseudopotential method with the local density approximation. We find that the charged nanocluster favors the diamond phase over the reconstruction into a fullerene-like structure. Occupying the dangling bond orbitals in the outermost surface, the excess charge can stabilize the bare diamond surface and destabilize the C-H bond on the hydrogenated surface. In combination with recent experimental results, our calculations suggest that negative charging could promote the nucleation and further growth of low-pressure diamond.open8

Serum screening with Down's syndrome markers to predict pre-eclampsia and small for gestational age: Systematic review and meta-analysis

<p>Abstract</p> <p>Background</p> <p>Reliable antenatal identification of pre-eclampsia and small for gestational age is crucial to judicious allocation of monitoring resources and use of preventative treatment with the prospect of improving maternal/perinatal outcome. The purpose of this systematic review was to determine the accuracy of five serum analytes used in Down's serum screening for prediction of pre-eclampsia and/or small for gestational age.</p> <p>Methods</p> <p>The data sources included Medline, Embase, Cochrane library, Medion (inception to February 2007), hand searching of relevant journals, reference list checking of included articles, contact with experts. Two reviewers independently selected the articles in which the accuracy of an analyte used in Downs's serum screening before the 25^thgestational week was associated with the occurrence of pre-eclampsia and/or small for gestational age without language restrictions. Two authors independently extracted data on study characteristics, quality and results.</p> <p>Results</p> <p>Five serum screening markers were evaluated. 44 studies, testing 169,637 pregnant women (4376 pre-eclampsia cases) and 86 studies, testing 382,005 women (20,339 fetal growth restriction cases) met the selection criteria. The results showed low predictive accuracy overall. For pre-eclampsia the best predictor was inhibin A>2.79MoM positive likelihood ratio 19.52 (8.33,45.79) and negative likelihood ratio 0.30 (0.13,0.68) (single study). For small for gestational age it was AFP>2.0MoM to predict birth weight < 10^thcentile with birth < 37 weeks positive likelihood ratio 27.96 (8.02,97.48) and negative likelihood ratio 0.78 (0.55,1.11) (single study). A potential clinical application using aspirin as a treatment is given as an example.</p> <p>There were methodological and reporting limitations in the included studies thus studies were heterogeneous giving pooled results with wide confidence intervals.</p> <p>Conclusion</p> <p>Down's serum screening analytes have low predictive accuracy for pre-eclampsia and small for gestational age. They may be a useful means of risk assessment or of use in prediction when combined with other tests.</p

Spontaneous sparse learning for PCM-based memristor neural networks

Neural networks trained by backpropagation have achieved tremendous successes on numerous intelligent tasks. However, naive gradient-based training and updating methods on memristors impede applications due to intrinsic material properties. Here, we built a 39nm 1Gb phase change memory (PCM) memristor array and quantified the unique resistance drift effect. On this basis, spontaneous sparse learning (SSL) scheme that leverages the resistance drift to improve PCM-based memristor network training is developed. During training, SSL regards the drift effect as spontaneous consistency-based distillation process that reinforces the array weights at the high-resistance state continuously unless the gradient-based method switches them to low resistance. Experiments show that the SSL not only helps the convergence of network with better performance and sparsity controllability without additional computation in handwritten digit classification. This work promotes the learning algorithms with the intrinsic properties of memristor devices, opening a new direction for development of neuromorphic computing chips