Relativistic description of magnetic moments in nuclei with doubly closed shells plus or minus one nucleon

Using the relativistic point-coupling model with density functional PC-PK1, the magnetic moments of the nuclei $^{207}$Pb, $^{209}$Pb, $^{207}$Tl and $^{209}$Bi with a $jj$ closed-shell core $^{208}$Pb are studied on the basis of relativistic mean field (RMF) theory. The corresponding time-odd fields, the one-pion exchange currents, and the first- and second-order corrections are taken into account. The present relativistic results reproduce the data well. The relative deviation between theory and experiment for these four nuclei is 6.1% for the relativistic calculations and somewhat smaller than the value of 13.2% found in earlier non-relativistic investigations. It turns out that the $\pi$ meson is important for the description of magnetic moments, first by means of one-pion exchange currents and second by the residual interaction provided by the $\pi$ exchange.Comment: 11 pages, 7 figure

Machine Learning Optimization of p-Type Transparent Conducting Films

p-Type transparent conducting materials (p-TCMs) are important components of optoelectronic devices including solar cells, photodetectors, displays, and flexible sensors. Cu-Zn-S thin films prepared by chemical bath deposition (CBD) can have both high transparency in the visible range (>80%) as well as excellent hole conductivity (>1000 S cm-1). However, the interplay between the deposition parameters in the CBD process (metal and sulfur precursor concentrations, temperature, pH, complexing agents, etc.) creates a multidimensional parameter space such that optimization for a specific application is challenging and time-consuming. Here we show that strategic design of experiment combined with machine learning (ML) allows for the efficient optimization of p-TCM performance. The approach is guided by a figure of merit (FOM) calculated from the film conductivity and optical transmission in the desired spectral range. A specific example is shown using two steps of optimization using a selected subset of four experimental CBD factors. The ML model is based on support vector regression employing a radial basis function as the kernel function. 10-fold cross-validation was performed to mitigate overfitting. After the first round of optimization, predicted areas in the parameter space with maximal FOMs were selected for a second round of optimization. Films with optimal FOMs were incorporated into heterojunction solar cells and transparent photodiodes. The optimization approach shown here will be generally applicable to any materials synthesis process with multiple parameters

GRBs and fundamental physics

Gamma-ray bursts (GRBs) are short and intense flashes at the cosmological distances, which are the most luminous explosions in the Universe. The high luminosities of GRBs make them detectable out to the edge of the visible universe. So, they are unique tools to probe the properties of high-redshift universe: including the cosmic expansion and dark energy, star formation rate, the reionization epoch and the metal evolution of the Universe. First, they can be used to constrain the history of cosmic acceleration and the evolution of dark energy in a redshift range hardly achievable by other cosmological probes. Second, long GRBs are believed to be formed by collapse of massive stars. So they can be used to derive the high-redshift star formation rate, which can not be probed by current observations. Moreover, the use of GRBs as cosmological tools could unveil the reionization history and metal evolution of the Universe, the intergalactic medium (IGM) properties and the nature of first stars in the early universe. But beyond that, the GRB high-energy photons can be applied to constrain Lorentz invariance violation (LIV) and to test Einstein's Equivalence Principle (EEP). In this paper, we review the progress on the GRB cosmology and fundamental physics probed by GRBs.Comment: 38 pages, 18 figures, Review based on ISSI workshop "Gamma-Ray Bursts: a Tool to Explore the Young Universe" (2015, Beijing, China), accepted for publication in Space Science Review

Effect of carbon nanotube doping on critical current density of MgB2 superconductor

The effect of doping MgB2 with carbon nanotubes on transition temperature, lattice parameters, critical current density and flux pinning was studied for MgB2-xCx with x = 0, 0.05, 0.1, 0.2 and 0.3. The carbon substitution for B was found to enhance Jc in magnetic fields but depress Tc. The depression of Tc, which is caused by the carbon substitution for B, increases with increasing doping level, sintering temperature and duration. By controlling the extent of the substitution and addition of carbon nanotubes we can achieve the optimal improvement on critical current density and flux pinning in magnetic fields while maintaining the minimum reduction in Tc. Under these conditions, Jc was enhanced by two orders of magnitude at 8T and 5K and 7T and 10K. Jc was more than 10,000A/cm2 at 20K and 4T and 5K and 8.5T, respectively

Quantum state engineering with flux-biased Josephson phase qubits by Stark-chirped rapid adiabatic passages

In this paper, the scheme of quantum computing based on Stark chirped rapid adiabatic passage (SCRAP) technique [L. F. Wei et al., Phys. Rev. Lett. 100, 113601 (2008)] is extensively applied to implement the quantum-state manipulations in the flux-biased Josephson phase qubits. The broken-parity symmetries of bound states in flux-biased Josephson junctions are utilized to conveniently generate the desirable Stark-shifts. Then, assisted by various transition pulses universal quantum logic gates as well as arbitrary quantum-state preparations could be implemented. Compared with the usual PI-pulses operations widely used in the experiments, the adiabatic population passage proposed here is insensitive the details of the applied pulses and thus the desirable population transfers could be satisfyingly implemented. The experimental feasibility of the proposal is also discussed.Comment: 9 pages, 4 figure

Filtering for nonlinear genetic regulatory networks with stochastic disturbances

In this paper, the filtering problem is investigated for nonlinear genetic regulatory networks with stochastic disturbances and time delays, where the nonlinear function describing the feedback regulation is assumed to satisfy the sector condition, the stochastic perturbation is in the form of a scalar Brownian motion, and the time delays exist in both the translation process and the feedback regulation process. The purpose of the addressed filtering problem is to estimate the true concentrations of the mRNA and protein. Specifically, we are interested in designing a linear filter such that, in the presence of time delays, stochastic disturbances as well as sector nonlinearities, the filtering dynamics of state estimation for the stochastic genetic regulatory network is exponentially mean square stable with a prescribed decay rate lower bound beta. By using the linear matrix inequality (LMI) technique, sufficient conditions are first derived for ensuring the desired filtering performance for the gene regulatory model, and the filter gain is then characterized in terms of the solution to an LMI, which can be easily solved by using standard software packages. A simulation example is exploited in order to illustrate the effectiveness of the proposed design procedures