Role of interactions in ferrofluid thermal ratchets

Orientational fluctuations of colloidal particles with magnetic moments may be rectified with the help of external magnetic fields with suitably chosen time dependence. As a result a noise-driven rotation of particles occurs giving rise to a macroscopic torque per volume of the carrier liquid. We investigate the influence of mutual interactions between the particles on this ratchet effect by studying a model system with mean-field interactions. The stochastic dynamics may be described by a nonlinear Fokker-Planck equation for the collective orientation of the particles which we solve approximately by using the effective field method. We determine an interval for the ratio between coupling strength and noise intensity for which a self-sustained rectification of fluctuations becomes possible. The ratchet effect then operates under conditions for which it were impossible in the absence of interactions.Comment: 18 pages, 10 figure

On the Influence of Magnetic Fields on the Structure of Protostellar Jets

We here present the first results of fully three-dimensional (3-D) MHD simulations of radiative cooling pulsed (time-variable) jets for a set of parameters which are suitable for protostellar outflows. Considering different initial magnetic field topologies in approximate $equipartition$ with the thermal gas, i.e., (i) a longitudinal, and (ii) a helical field, both of which permeating the jet and the ambient medium; and (iii) a purely toroidal field permeating only the jet, we find that the overall morphology of the pulsed jet is not very much affected by the presence of the different magnetic field geometries in comparison to a nonmagnetic calculation. Instead, the magnetic fields tend to affect essentially the detailed structure and emission properties behind the shocks at the head and at the pulse-induced internal knots, particularly for the helical and toroidal geometries. In these cases, we find, for example, that the $H_\alpha$ emissivity behind the internal knots can be about three to four times larger than that of the purely hydrodynamical jet. We also find that some features, like the nose cones that often develop at the jet head in 2-D calculations involving toroidal magnetic fields, are smoothed out or absent in the 3-D calculations.Comment: 13 pages, 3 figures, Accepted by ApJ Letters after minor corrections (for high resolution figures, see http://www.iagusp.usp.br/~adriano/h.tar

Collisional Quenching of High Rotational Levels in A_2_+ OH

Collisional removal of the v_=0 level of the A_2_+ state of the OH radical has been studied as a function of rotational level N_ at room temperature. OH in high rotational levels of the X_2_i state were created by 193 nm photolysis of HNO3 and excited to A_2_+ by a tunable dye laser. Time decays of fluorescence at varying pressures were measured. For O2 and H2, the quenching cross section _Q decreased with increasing N_ until N__10; for higher N_ it appears to remain approximately constant. Xe behaves the same way except that the decrease continues to N_=15. For Kr, _Q appears to decrease to within experimental error of zero at N_=10; and for N2 it was within error of zero above N_=10. These results have implications for laser-induced fluorescence atmospheric monitoring of OH and combustion temperature determinations, as well as a fundamental understanding of collisional quenching. Quenching of OH, N__1, by HNO3 was found to be 81±8 A2.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/86761/1/Sick30.pd

Realistic atomistic structure of amorphous silicon from machine-learning-driven molecular dynamics

Amorphous silicon (a-Si) is a widely studied noncrystalline material, and yet the subtle details of its atomistic structure are still unclear. Here, we show that accurate structural models of a-Si can be obtained using a machine-learning-based interatomic potential. Our best a-Si network is obtained by simulated cooling from the melt at a rate of 1011 K/s (that is, on the 10 ns time scale), contains less than 2% defects, and agrees with experiments regarding excess energies, diffraction data, and 29Si NMR chemical shifts. We show that this level of quality is impossible to achieve with faster quench simulations. We then generate a 4096-atom system that correctly reproduces the magnitude of the first sharp diffraction peak (FSDP) in the structure factor, achieving the closest agreement with experiments to date. Our study demonstrates the broader impact of machine-learning potentials for elucidating structures and properties of technologically important amorphous materials