1,407 research outputs found
Unusual superparamagnetic behavior of Co3O4 nanoparticles
We report detailed studies on magnetic properties of Co3O4 nanoparticles of
average size 12.5 nm. Temperature and field dependence of magnetization, wait
time dependence of magnetic relaxation (aging), memory effects and temperature
dependence of specific heat have been investigated to understand the magnetic
behavior of these particles. We find that the particles show some features
characteristic of nanoparticle magnetism such as bifurcation of field cooled
(FC) and zero field cooled (ZFC) susceptibilities and a slow relaxation of
magnetization. However, strangely, the temperature at which ZFC magnetization
peaks coincides with the bifurcation temperature and does not shift on
application of magnetic fields up to 1 kOe, unlike most other nanoparticle
systems. Aging effects in these particles are negligible in both FC and ZFC
protocol and memory effects are present only in FC protocol. Our results show
that Co3O4 nanoparticles constitute a unique system where superparamagnetic
blocking starts above the N\'eel temperature, in the paramagnetic state.Comment: 5 pages, 4 figure
Memory, Aging and Spin Glass Nature: A Study of NiO Nanoparticles
We report studies on magnetization dynamics in NiO nanoparticles of average
size 5 nm. Temperature and time dependence of dc magnetization, wait time
dependence of magnetic relaxation (aging) and memory phenomena in the dc
magnetization are studied with various temperature and field protocols. We
observe that the system shows memory and aging in field cooled and zero field
cooled magnetization measurements. These experiments show that the magnetic
behavior of NiO nanoparticles is similar to spin glasses. We argue that the
spin glass behavior originates from the freezing of spins at the surface of the
individual particles.Comment: 5 pages, 4 figures typos adde
An Efficient Deep Learning Technique for the Navier-Stokes Equations: Application to Unsteady Wake Flow Dynamics
We present an efficient deep learning technique for the model reduction of
the Navier-Stokes equations for unsteady flow problems. The proposed technique
relies on the Convolutional Neural Network (CNN) and the stochastic gradient
descent method. Of particular interest is to predict the unsteady fluid forces
for different bluff body shapes at low Reynolds number. The discrete
convolution process with a nonlinear rectification is employed to approximate
the mapping between the bluff-body shape and the fluid forces. The deep neural
network is fed by the Euclidean distance function as the input and the target
data generated by the full-order Navier-Stokes computations for primitive bluff
body shapes. The convolutional networks are iteratively trained using the
stochastic gradient descent method with the momentum term to predict the fluid
force coefficients of different geometries and the results are compared with
the full-order computations. We attempt to provide a physical analogy of the
stochastic gradient method with the momentum term with the simplified form of
the incompressible Navier-Stokes momentum equation. We also construct a direct
relationship between the CNN-based deep learning and the Mori-Zwanzig formalism
for the model reduction of a fluid dynamical system. A systematic convergence
and sensitivity study is performed to identify the effective dimensions of the
deep-learned CNN process such as the convolution kernel size, the number of
kernels and the convolution layers. Within the error threshold, the prediction
based on our deep convolutional network has a speed-up nearly four orders of
magnitude compared to the full-order results and consumes an insignificant
fraction of computational resources. The proposed CNN-based approximation
procedure has a profound impact on the parametric design of bluff bodies and
the feedback control of separated flows.Comment: 49 pages, 12 figure
Paramagnetic to Superparamagnetic Transition in Ni(OH)_2 Nanoparticles
We report the temperature and field dependence of dc magnetization on sol-gel
prepared nanoparticles of Ni(OH)_2. At higher temperature the system is found
to behave as a paramagnet while we find evidence for superparamagnetic blocking
at low temperature. The system shows a paramagnet-superparamagnet transition
and we discuss the underlying mechanism.Comment: 4 pages, 7 figures, a floating reference remove
Magnetic Susceptibility of NiO Nanoparticles
Nickel oxide nanoparticles of different sizes are prepared and characterized
by x-ray diffraction and transmission electron microscopy. A.C. susceptibility
measurements as a function of temperature are carried out for various particle
sizes and frequencies. We find that the behavior of the system is spin glass
like.Comment: 16 pages, 5 figure
Further Evidences for Spin Glass like behavior in NiO nanoparticles
Nickel oxide nanoparticles are prepared by a sol gel method and characterized
by x-ray diffraction and transmission electron microscope. Here we present
measurements on temperature and field dependence of magnetization and time
dependence of thermoremanent magnetization. Our conclusion based on these
measurements is that the system shows spin glass like behavior.Comment: 5 pages, 3 figure
Evidence for topological surface states in metallic single crystals of Bi2Te3
Bi2Te3 is a member of a new class of materials known as topological
insulators which are supposed to be insulating in the bulk and conducting on
the surface. However experimental verification of the surface states has been
difficult in electrical transport measurements due to a conducting bulk. We
report low temperature magnetotransport measurements on single crystal samples
of Bi2Te3. We observe metallic character in our samples and large and linear
magnetoresistance from 1.5 K to 290 K with prominent Shubnikov-de Haas (SdH)
oscillations whose traces persist upto 20 K. Even though our samples are
metallic we are able to obtain a Berry phase close to the value of {\pi}
expected for Dirac fermions of the topological surface states. This indicates
that we might have obtained evidence for the topological surface states in
metallic single crystals of Bi2Te3. Other physical quantities obtained from the
analysis of the SdH oscillations are also in close agreement with those
reported for the topological surface states. The linear magnetoresistance
observed in our sample, which is considered as a signature of the Dirac
fermions of the surface states, lends further credence to the existence of
topological surface states
Anomalous magnetic behavior of CuO nanoparticles
We report studies on temperature, field and time dependence of magnetization
on cupric oxide nanoparticles of sizes 9 nm, 13 nm and 16 nm. The nanoparticles
show unusual features in comparison to other antiferromagnetic nanoparticle
systems. The field cooled (FC) and zero field cooled (ZFC) magnetization curves
bifurcate well above the N\'eel temperature and the usual peak in the ZFC
magnetization curve is absent.
The system does not show any memory effects which is in sharp contrast to the
usual behavior shown by other antiferromagnetic nanoparticles. It turns out
that the non-equilibrium behavior of CuO nanoparticles is very strange and is
neither superparamagnetic nor spin glass-like.Comment: 8 pages, 5 figure
Spin glass behavior of gelatin coated NiO nanoparticles
We report magnetic studies on gelatin coated NiO nanoparticles of average
size 7 nm. Temperature and time dependence of dc magnetization, wait time
dependence of magnetic relaxation (aging), memory effects in the dc
magnetization and frequency dependence of ac susceptibility have been
investigated. We observe that the magnetic behavior of coated NiO nanoparticles
differs substantially from that of bare nanoparticles. The magnetic moment of
the particles is highly enhanced and the ZFC magnetization data displays a
sharp peak (Tp1 = 15 K) at a low temperature in addition to a usual high
temperature peak (Tp2 =170 K). We observe that this system exhibits various
features characteristic of spin glass like behavior and Tp2 corresponds to the
average freezing temperature. We argue that this behavior is due to surface
spin freezing within a particle. The nature of the low temperature peak is,
however, ambiguous as below Tp1 some features observed are characteristic of
superparamagnetic blocking while some other features correspond to spin glass
like behavior.Comment: 8 pages, 10 figure
Unusual non-equilibrium behavior of cupric oxide nanoparticles
We report studies on temperature, field and time dependence of magnetization
on cupric oxide nanoparticles of sizes 9 nm, 13 nm and 16 nm. The nanoparticles
show unusual features in comparison to other antiferromagnetic nanoparticle
systems. The field cooled (FC) and zero field cooled (ZFC) magnetization curves
bifurcate well above the Neel temperature and the usual peak in the ZFC
magnetization curve is absent.
The system does not show any memory effects which is in sharp contrast to the
usual behavior shown by other antiferromagnetic nanoparticles. It turns out
that the nature of CuO nanoparticles is very strange and is neither
superparamagnetic nor spin glass-like .Comment: 5 pages,4 figure
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