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$_o$) 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$_o$ 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$_2$ 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 2155$-$304 occurs in the UV-EUV region and hence its X-ray emission (0.6--10 keV) lies mostly in the falling part of the synchrotron hump. We aim to study the X-ray emission of PKS 2155$-$304 during different intensity states in 2009$-$2014 using XMM$-$Newton 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