981 research outputs found
Photoluminescence transient study of surface defects in ZnO nanorods grown by chemical bath deposition
Two deep level defects (2.25 and 2.03 eV) associated with oxygen vacancies
(V) were identified in ZnO nanorods (NRs) grown by low cost chemical bath
deposition. A transient behaviour in the photoluminescence (PL) intensity of
the two V states was found to be sensitive to the ambient environment and
to NR post-growth treatment. The largest transient was found in samples dried
on a hot plate with a PL intensity decay time, in air only, of 23 and 80 s for
the 2.25 and 2.03 eV peaks, respectively. Resistance measurements under UV
exposure exhibited a transient behaviour in full agreement with the PL
transient indicating a clear role of atmospheric O on the surface defect
states. A model for surface defect transient behaviour due to band bending with
respect to the Fermi level is proposed. The results have implications for a
variety of sensing and photovoltaic applications of ZnO NRs
The Hard X-ray emission of the blazar PKS 2155--304
The synchrotron peak of the X-ray bright High Energy Peaked Blazar (HBL) PKS
2155304 occurs in the UV-EUV region and hence its X-ray emission (0.6--10
keV) lies mostly in the falling part of the synchrotron hump. We aim to study
the X-ray emission of PKS 2155304 during different intensity states in
20092014 using XMMNewton satellite. We studied the spectral curvature of
all of the observations to provide crucial information on the energy
distribution of the non-thermal particles. Most of the observations show
curvature or deviation from a single power-law and can be well modeled by a log
parabola model. In some of the observations, we find spectral flattening after
6 keV. In order to find the possible origin of the X-ray excess, we built the
Multi-band Spectral Energy distribution (SED). We find that the X-ray excess in
PKS 2155--304 is difficult to fit in the one zone model but, could be easily
reconciled in the spine/layer jet structure. The hard X-ray excess can be
explained by the inverse Comptonization of the synchrotron photons (from the
layer) by the spine electrons.Comment: 14 pages, 7 Figures, Accepted for publication in Ap
A study of physicochemical properties, volatile component analysis and antioxidative properties of honey
Honey samples from five different floral origins were analysed through solid phase microextraction (SPME) with objective to identify and compare their volatile organic compound profile. In addition, the levels of water, HMF, free proline, total acidity, diastase activity and sugar content have also been reported. The samples showed adequate water and HMF content. Total phenolics varied from 75.6 to 98.5mg/g, while total flavonoids were comprised between 1.86 and 4.93 mg/g, expressed as quercetin equivalents (the lowest and highest values were also found for Eucalyptus honey and neem honey, respectively). The IC50 value for DPPH has been found to be ranged from 4.97 to 9.45mg/ml. The highest DPPH RSA was found in Eucalyptus honey, followed by mustard honey and neem hone
Single-particle and collective excitations in quantum wires made up of vertically stacked quantum dots: Zero magnetic field
We report on the theoretical investigation of the elementary electronic
excitations in a quantum wire made up of vertically stacked self-assembled
InAs/GaAs quantum dots. The length scales (of a few nanometers) involved in the
experimental setups prompt us to consider an infinitely periodic system of
two-dimensionally confined (InAs) quantum dot layers separated by GaAs spacers.
The the Bloch functions and the Hermite functions together characterize the
whole system. We then make use of the Bohm-Pines' (full) random-phase
approximation in order to derive a general nonlocal, dynamic dielectric
function. Thus developed theoretical framework is then specified to work within
a (lowest miniband and) two-subband model that enables us to scrutinize the
single-particle as well as collective responses of the system. We compute and
discuss the behavior of the eigenfunctions, band-widths, density of states,
Fermi energy, single-particle and collective excitations, and finally size up
the importance of studying the inverse dielectric function in relation with the
quantum transport phenomena. It is remarkable to notice how the variation in
the barrier- and well-widths can allow us to tailor the excitation spectrum in
the desired energy range. Given the advantage of the vertically stacked quantum
dots over the planar ones and the foreseen applications in the single-electron
devices and in the quantum computation, it is quite interesting and important
to explore the electronic, optical, and transport phenomena in such systems
Vision-Based Intelligent Robot Grasping Using Sparse Neural Network
In the modern era of Deep Learning, network parameters play a vital role in
models efficiency but it has its own limitations like extensive computations
and memory requirements, which may not be suitable for real time intelligent
robot grasping tasks. Current research focuses on how the model efficiency can
be maintained by introducing sparsity but without compromising accuracy of the
model in the robot grasping domain. More specifically, in this research two
light-weighted neural networks have been introduced, namely Sparse-GRConvNet
and Sparse-GINNet, which leverage sparsity in the robotic grasping domain for
grasp pose generation by integrating the Edge-PopUp algorithm. This algorithm
facilitates the identification of the top K% of edges by considering their
respective score values. Both the Sparse-GRConvNet and Sparse-GINNet models are
designed to generate high-quality grasp poses in real-time at every pixel
location, enabling robots to effectively manipulate unfamiliar objects. We
extensively trained our models using two benchmark datasets: Cornell Grasping
Dataset (CGD) and Jacquard Grasping Dataset (JGD). Both Sparse-GRConvNet and
Sparse-GINNet models outperform the current state-of-the-art methods in terms
of performance, achieving an impressive accuracy of 97.75% with only 10% of the
weight of GR-ConvNet and 50% of the weight of GI-NNet, respectively, on CGD.
Additionally, Sparse-GRConvNet achieve an accuracy of 85.77% with 30% of the
weight of GR-ConvNet and Sparse-GINNet achieve an accuracy of 81.11% with 10%
of the weight of GI-NNet on JGD. To validate the performance of our proposed
models, we conducted extensive experiments using the Anukul (Baxter) hardware
cobot
Au9+ swift heavy ion irradiation of Zn[CS(NH2)2]3SO4 crystal: Crystalline perfection and optical properties
The single crystal of tris(thiourea)zinc sulphate (Zn[CS(NH2)2]3SO4) was
irradiated by 150 MeV Au9+ swift heavy ions and analyzed in comparison with
pure crystal for crystalline perfection and optical properties. The Fourier
transform infrared and x-ray powder diffraction inferred that swift ions lead
the disordering and breaking of molecular bonds in lattice without formation of
new structural phases. High resolution X-ray diffraction (HRXRD) revealed the
abundance of point defects, and formation of mosaics and low angle grain
boundaries in the irradiated region of crystal. The swift ion irradiation found
to affect the lattice vibrational modes and functional groups significantly.
The defects induced by heavy ions act as the color centers and resulted in
enhance of photoluminescence emission intensity. The optical transparency and
band gap found to be decreased.Comment: 7 page
Context-aware 6D Pose Estimation of Known Objects using RGB-D data
6D object pose estimation has been a research topic in the field of computer
vision and robotics. Many modern world applications like robot grasping,
manipulation, autonomous navigation etc, require the correct pose of objects
present in a scene to perform their specific task. It becomes even harder when
the objects are placed in a cluttered scene and the level of occlusion is high.
Prior works have tried to overcome this problem but could not achieve accuracy
that can be considered reliable in real-world applications. In this paper, we
present an architecture that, unlike prior work, is context-aware. It utilizes
the context information available to us about the objects. Our proposed
architecture treats the objects separately according to their types i.e;
symmetric and non-symmetric. A deeper estimator and refiner network pair is
used for non-symmetric objects as compared to symmetric due to their intrinsic
differences. Our experiments show an enhancement in the accuracy of about 3.2%
over the LineMOD dataset, which is considered a benchmark for pose estimation
in the occluded and cluttered scenes, against the prior state-of-the-art
DenseFusion. Our results also show that the inference time we got is sufficient
for real-time usage
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