We present a three dimensional microscopic particle in cell code. The code models nanoplasmas in intense laser fields, taking account of all relevant microscopic interactions. Our simulation reveals the physical processes determining the laser induced explosion of large clusters with several 10,000 atoms.
The interaction of noble gas clusters with intense, VUV radiation is investigated by molecular dynamics simulations. It is shown that the free-electron laser cluster interaction creates a strongly coupled plasma. A new heating mechanism is identified that is more efficient than inverse Bremsstrahlung heating and explains the observation of unusually high charge states in recent experiments at DESY. The heating mechanism is a consequence of the strongly coupled plasma dynamics, in which collisional processes are strongly modified. Energy absorption takes place in the following cycle: many-body collisions, resulting in an enhanced recombination of free electrons to exited states, and subsequent reionization.
The interaction of intense laser pulses with noble gas clusters is investigated by a molecular dynamics analysis. We find that the strength of electron–ion coupling in the created nanoplasmas (Γei), and thus the collisional properties, can be controlled by a single parameter: the laser intensity. Varying the intensity from 1016 to 2 × 1014 W cm−2 results in nanoplasmas with Γei between 0.1 and 1. This spans the range of classical kinetic physics, from weakly coupled plasmas dominated by collective behaviour, to strongly coupled plasmas dominated by collisions. In combination with recent advances in ultrafast technology, this opens novel avenues for a systematic investigation of collective and collision processes in strongly coupled plasmas, taking place on sub-femtosecond time scales.
Despite the significant progress made in recent years, the computation of the complete set of elementary flux modes of large or even genome-scale metabolic networks is still impossible. We introduce a novel approach to speed up the calculation of elementary flux modes by including transcriptional regulatory information into the analysis of metabolic network. Taking into account gene regulation dramatically reduces the solution space and allows the presented algorithm to constantly eliminate biologically infeasible modes at an early stage of the computation procedure. Thereby, the computational costs, such as runtime, memory usage and disk space are considerably reduced. Consequently, using the presented mode elimination algorithm pushes the size of metabolic networks that can be studied by elementary flux modes to new limits.
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