140 research outputs found
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 , where from numerical data. On the other hand, when the particle moves faster
than the surface, its dynamics is controlled by the surface fluctuations and
. A self-consistent approximation predicts that the
anomalous diffusion exponent is , 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
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