Transient analysis of piecewise homogeneous Markov fluid models

Piecewise homogeneous Markov fluid models are composed by homogeneous intervals where the model is governed by an interval dependent pair of generators and the model behaviour changes at the boundaries. The main difficulty of the transient analysis of piecewise homogeneous Markov fluid models is the appropriate description of the various boundary cases. The paper proposes an analytical approach to handle the wide variety of the possible boundary cases in a relatively simple to describe and implement manner.

Incoherent superconductivity well above Tc in high-Tc cuprates - harmonizing the spectroscopic and thermodynamic data

© 2017 IOP Publishing Ltd and Deutsche Physikalische Gesellschaft. Cuprate superconductors have long been known to exhibit an energy gap that persists high above the superconducting transition temperature (Tc). Debate has continued now for decades as to whether it is a precursor superconducting gap or a pseudogap arising from some competing correlation. Failure to resolve this has arguably delayed explaining the origins of superconductivity in these highly complex materials. Here we effectively settle the question by calculating a variety of thermodynamic and spectroscopic properties, exploring the effect of a temperature-dependent pair-breaking term in the self-energy in the presence of pairing interactions that persist well above Tc.We start by fitting the detailed temperature-dependence of the electronic specific heat and immediately can explain its hitherto puzzling field dependence. Taking this same combination of pairing temperature and pairbreaking scattering we are then able to simultaneously describe in detail the unusual temperature and field dependence of the superfluid density, tunneling, Raman and optical spectra, which otherwise defy explanation in terms a superconducting gap that closes conventionally at Tc. These findings demonstrate that the gap above Tc in the overdoped regime likely originates from incoherent superconducting correlations, and is distinct from the competing-order pseudogap that appears at lower doping.

Incoherent superconductivity well above Tc in high-Tc cuprates - harmonizing the spectroscopic and thermodynamic data

© 2017 IOP Publishing Ltd and Deutsche Physikalische Gesellschaft. Cuprate superconductors have long been known to exhibit an energy gap that persists high above the superconducting transition temperature (Tc). Debate has continued now for decades as to whether it is a precursor superconducting gap or a pseudogap arising from some competing correlation. Failure to resolve this has arguably delayed explaining the origins of superconductivity in these highly complex materials. Here we effectively settle the question by calculating a variety of thermodynamic and spectroscopic properties, exploring the effect of a temperature-dependent pair-breaking term in the self-energy in the presence of pairing interactions that persist well above Tc.We start by fitting the detailed temperature-dependence of the electronic specific heat and immediately can explain its hitherto puzzling field dependence. Taking this same combination of pairing temperature and pairbreaking scattering we are then able to simultaneously describe in detail the unusual temperature and field dependence of the superfluid density, tunneling, Raman and optical spectra, which otherwise defy explanation in terms a superconducting gap that closes conventionally at Tc. These findings demonstrate that the gap above Tc in the overdoped regime likely originates from incoherent superconducting correlations, and is distinct from the competing-order pseudogap that appears at lower doping.

Reversible and non-reversible motion of NdFeB-particles in magnetorheological elastomers

Magnetorheological elastomers are a class of smart hybrid material where magnetic microparticles are embedded in an elastomer matrix. The combination of elastic and magnetic properties leads to highly complex material behaviour, which is strongly affected by the arrangement and the magnetically induced motion of the magnetic particles. Thus, the knowledge of the internal particle structure is key to gain a deeper understanding of the complex material behaviour. In this paper, X-ray microtomography was applied to analyse the internal particle structure of a magnetorheological elastomer containing magnetically hard NdFeB-particles. A stepwise magnetisation of the material enabled a detailed characterisation of the occurring non-reversible chain formation process. Furthermore, the application of magnetic fields during measurements enabled an analysis of a mostly reversible particle motion occurring in connection with the magnetorheological effect. The collected tomography data of the particle structure was evaluated on a single particle basis as well as by means of a direction-dependent pair correlation function. To provide a scale bridging between macroscopic and microscopic properties, the found results regarding the particle motion were linked to mechanical and magnetic properties of the material.

Time and rate dependent synaptic learning in neuro-mimicking resistive memories

Memristors have demonstrated immense potential as building blocks in future adaptive neuromorphic architectures. Recently, there has been focus on emulating specific synaptic functions of the mammalian nervous system by either tailoring the functional oxides or engineering the external programming hardware. However, high device-to-device variability in memristors induced by the electroforming process and complicated programming hardware are among the key challenges that hinder achieving biomimetic neuromorphic networks. Here, a simple hybrid complementary metal oxide semiconductor (CMOS)-memristor approach is reported to implement different synaptic learning rules by utilizing a CMOS-compatible memristor based on oxygen-deficient SrTiO3-x (STOx). The potential of such hybrid CMOS-memristor approach is demonstrated by successfully imitating time-dependent (pair and triplet spike-time-dependent-plasticity) and rate-dependent (Bienenstosk-Cooper-Munro) synaptic learning rules. Experimental results are benchmarked against in-vitro measurements from hippocampal and visual cortices with good agreement. The scalability of synaptic devices and their programming through a CMOS drive circuitry elaborates the potential of such an approach in realizing adaptive neuromorphic computation and networks.