Dynamics of a passive sliding particle on a randomly fluctuating surface

We study the motion of a particle sliding under the action of an external field on a stochastically fluctuating one-dimensional Edwards-Wilkinson surface. Numerical simulations using the single-step model shows that the mean-square displacement of the sliding particle shows distinct dynamic scaling behavior, depending on whether the surface fluctuates faster or slower than the motion of the particle. When the surface fluctuations occur on a time scale much smaller than the particle motion, we find that the characteristic length scale shows anomalous diffusion with $\xi(t)\sim t^{2\phi}$, where $\phi\approx 0.67$ from numerical data. On the other hand, when the particle moves faster than the surface, its dynamics is controlled by the surface fluctuations and $\xi(t)\sim t^{{1/2}}$. A self-consistent approximation predicts that the anomalous diffusion exponent is $\phi={2/3}$, in good agreement with simulation results. We also discuss the possibility of a slow cross-over towards asymptotic diffusive behavior. The probability distribution of the displacement has a Gaussian form in both the cases.Comment: 6 pages, 4 figures, error in reference corrected and new reference added, submitted to Phys. Rev.

Encapsulation of Magnetic Nanoparticles with Biopolymer for Biomedical Application

Abstract—Magnetite nanoparticles were synthesized by coprecipitation of Fe2+ and Fe3+ with NH4OH using Spinning Disc Processing (SDP). Chitosan was then coated on the surface of magnetite nanoparticles using SDP. FTIR study and zeta potential measurement confirmed the absorption of chitosan unto the surface of magnetite nanoparticles. Transmission electron microscope (TEM) image showed that the particle sizes are in the range 10 – 200 nm

Green Synthesis of Magnetite Nanoparticles (via Thermal Decomposition Method) with Controllable Size and Shape

Magnetite (Fe3O4) nanoparticles with controllable size and shape were synthesized by the thermal decomposition method. In contrast to previously reported thermal decomposition methods, our synthesis method had utilized a much cheaper and less toxic iron precursor, iron acetylacetonate (Fe(acac)3), and environmentally benign and non-toxic polyethylene oxide (PEO) was being used as the solvent and surfactant simultaneously. Fe3O4 nanoparticles of controllable size and shape were prepared by manipulating the synthesis parameters such as precursor concentrations, reaction durations and surfactants

Fabrication of carbon nano-tubes decorated with ultra fine superparamagnetic nano-particles under continuous flow conditions

Ultra fine (2–3 nm) magnetite (Fe3O4) nano-particles are uniformly deposited on single-walled carbon nano-tubes (SWCNTs) pre-functionalised with carboxylic acid groups using microwave radiation. The deposition process involves chemical precipitation associated with continuous flow spinning disc processing (SDP), as a rapid, environmentally friendly approach which is readily scalable for large scale synthesis. The resulting decorated SWCNTs are superparamagnetic with specific saturated magnetization of 30 emu g−1

Magnetite ferrofluids stabilized by sulfonato-calixarenes

Magnetite (Fe304) nanoparticles stabilised by sulfonatocalixarene macrocycles are readily accessible by a rapid in situ co-precipitation, and exhibit ferro-fluidic and superparamagnetic behaviour

Outlier-robust manifold pre-integration for INS/GPS fusion

We tackle the INS/GPS sensor fusion problem for pose estimation, particularly in the common setting where the INS components (IMU and magnetometer) function at much higher frequencies than GPS, and where the magnetometer and GPS are prone to giving erroneous measurements (outliers) due to magnetic disturbances and glitches. Our main contribution is a novel non-linear optimization framework that (1) fuses preintegrated IMU and magnetometer measurements with GPS, in a manner that respects the manifold structure of the state space; and (2) supports the usage of robust norms and efficient large scale optimization to effectively mitigate the effects of outliers. Through extensive experiments, we demonstrate the superior accuracy and robustness of our approach over filtering methods (which are customarily applied in the target setting) with minimal impact to computational efficiency. Our work further illustrates the strength of optimization approaches in state estimation problems and paves the way for their adoption in the control and navigation communities.Shin-Fang Ch'ng, Alireza Khosravian, Anh-Dzung Doan and Tat-Jun Chi

Family Unification, Exotic States and Light Magnetic Monopoles

Models with fermions in bifundamental representations can lead naturally to family unification as opposed to family replication. Such models typically predict (exotic) color singlet states with fractional electric charge, and magnetic monopoles with multiple Dirac charge. The exotics may be at the TeV scale, and relatively light magnetic monopoles (greater than about 10^7 GeV) can be present in the galaxy with abundance near the Parker bound. We focus on three family SU(4)XSU(3)XSU(3) models.Comment: 37 page

Directed avalanche processes with underlying interface dynamics

We describe a directed avalanche model; a slowly unloading sandbox driven by lowering a retaining wall. The directness of the dynamics allows us to interpret the stable sand surfaces as world sheets of fluctuating interfaces in one lower dimension. In our specific case, the interface growth dynamics belongs to the Kardar-Parisi-Zhang (KPZ) universality class. We formulate relations between the critical exponents of the various avalanche distributions and those of the roughness of the growing interface. The nonlinear nature of the underlying KPZ dynamics provides a nontrivial test of such generic exponent relations. The numerical values of the avalanche exponents are close to the conventional KPZ values, but differ sufficiently to warrant a detailed study of whether avalanche correlated Monte Carlo sampling changes the scaling exponents of KPZ interfaces. We demonstrate that the exponents remain unchanged, but that the traces left on the surface by previous avalanches give rise to unusually strong finite-size corrections to scaling. This type of slow convergence seems intrinsic to avalanche dynamics.Comment: 13 pages, 13 figure

An Interface View of Directed Sandpile Dynamics

We present a directed unloading sand box type avalanche model, driven by slowly lowering the retaining wall at the bottom of the slope. The avalanche propagation in the two dimensional surface is related to the space-time configurations of one dimensional Kardar-Parisi-Zhang (KPZ) type interface growth dynamics. We express the scaling exponents for the avalanche cluster distributions into that framework. The numerical results agree closely with KPZ scaling, but not perfectly.Comment: 4 pages including 5 figure

Growth model with restricted surface relaxation

We simulate a growth model with restricted surface relaxation process in d=1 and d=2, where d is the dimensionality of a flat substrate. In this model, each particle can relax on the surface to a local minimum, as the Edwards-Wilkinson linear model, but only within a distance s. If the local minimum is out from this distance, the particle evaporates through a refuse mechanism similar to the Kim-Kosterlitz nonlinear model. In d=1, the growth exponent beta, measured from the temporal behavior of roughness, indicates that in the coarse-grained limit, the linear term of the Kardar-Parisi-Zhang equation dominates in short times (low-roughness) and, in asymptotic times, the nonlinear term prevails. The crossover between linear and nonlinear behaviors occurs in a characteristic time t_c which only depends on the magnitude of the parameter s, related to the nonlinear term. In d=2, we find indications of a similar crossover, that is, logarithmic temporal behavior of roughness in short times and power law behavior in asymptotic times