Large Anomalous Hall effect in a silicon-based magnetic semiconductor

Magnetic semiconductors are attracting high interest because of their potential use for spintronics, a new technology which merges electronics and manipulation of conduction electron spins. (GaMn)As and (GaMn)N have recently emerged as the most popular materials for this new technology. While Curie temperatures are rising towards room temperature, these materials can only be fabricated in thin film form, are heavily defective, and are not obviously compatible with Si. We show here that it is productive to consider transition metal monosilicides as potential alternatives. In particular, we report the discovery that the bulk metallic magnets derived from doping the narrow gap insulator FeSi with Co share the very high anomalous Hall conductance of (GaMn)As, while displaying Curie temperatures as high as 53 K. Our work opens up a new arena for spintronics, involving a bulk material based only on transition metals and Si, and which we have proven to display a variety of large magnetic field effects on easily measured electrical properties.Comment: 19 pages with 5 figure

Computational study of noise in a large signal transduction network

<p>Abstract</p> <p>Background</p> <p>Biochemical systems are inherently noisy due to the discrete reaction events that occur in a random manner. Although noise is often perceived as a disturbing factor, the system might actually benefit from it. In order to understand the role of noise better, its quality must be studied in a quantitative manner. Computational analysis and modeling play an essential role in this demanding endeavor.</p> <p>Results</p> <p>We implemented a large nonlinear signal transduction network combining protein kinase C, mitogen-activated protein kinase, phospholipase A2, and <it>β </it>isoform of phospholipase C networks. We simulated the network in 300 different cellular volumes using the exact Gillespie stochastic simulation algorithm and analyzed the results in both the time and frequency domain. In order to perform simulations in a reasonable time, we used modern parallel computing techniques. The analysis revealed that time and frequency domain characteristics depend on the system volume. The simulation results also indicated that there are several kinds of noise processes in the network, all of them representing different kinds of low-frequency fluctuations. In the simulations, the power of noise decreased on all frequencies when the system volume was increased.</p> <p>Conclusions</p> <p>We concluded that basic frequency domain techniques can be applied to the analysis of simulation results produced by the Gillespie stochastic simulation algorithm. This approach is suited not only to the study of fluctuations but also to the study of pure noise processes. Noise seems to have an important role in biochemical systems and its properties can be numerically studied by simulating the reacting system in different cellular volumes. Parallel computing techniques make it possible to run massive simulations in hundreds of volumes and, as a result, accurate statistics can be obtained from computational studies.</p