Fabrication and characterization of Al2O3 nanoparticle reinforced aluminium matrix composite via powder metallurgy

In this study, aluminium-aluminium oxide (Al-Al2O3) metal matrix nanocomposites (MMNCs) with the different volume content of Al2O3 reinforcement were prepared. Three different types Al- Al2O3 nanocomposite specimens comprise of 10%, 15% and 20% volume fractions of Al2O3 were fabricated using conventional powder metallurgy (PM) route and their microstructure and mechanical properties were determined. The samples were prepared under 200 kN compaction load and 630 °C sintering temperature. The correlation between microstructure and mechanical properties due to the inclusion of Al2O3 nanoparticles were investigated. The optical micrographs revealed that the Al2O3 nanoparticles are almost uniformly distributed in the Al matrix with good bonding between matrix and reinforcement. Moreover, the mechanical properties including hardness, tensile strength and compressive strength of the nanocomposite increase with increasing volume fraction of the reinforcement. However, the impact strength decreases once the Al2O3 nanoparticles increase in the composite

Graphene-based nanocomposites and their fabrication, mechanical properties and applications

Graphene, the thinnest two-dimensional atomic material, has immerged as a revolutionary material and sparked a flurry of research and innovation owing to its outstanding mechanical, electrical, optical and thermal properties as well as high specific surface area. Graphenebased materials and their composites possess promising applications in a wide range of fields such as sensors, actuators, electronics, biomedical aids and membranes. In this review paper, a critical and comprehensive review has been carried out on the synthesis process and mechanical properties of graphene and graphene-based nanocomposites. Firstly, the concept and structure of graphene materials are discussed then different synthesis techniques and their advantages and limitations have been reviewed. The addition of graphene and its derivatives in producing different polymer and metal-based nanocomposite as well as fabricating hybrid nanocomposite has been thoroughly reviewed. Almost all the papers show that the presence of graphene even at very low loadings can provide significant improvement to the final material. Besides, other parameters that affect the nanocomposite are thoroughly reviewed. Furthermore, the perspective application of graphene materials and its nanocomposite in different promising fields has been discussed

Investigation on microstructure and hardness of nickel-alumina functionally graded material

In this research study, six-layered nickel-alumina (Ni-Al2O3) functionally graded material (FGM) was prepared using powder metallurgy (PM) method. The objectives of this study were to investigate the microstructure and hardness of the graded composite layer by layer. Using a cylindrical steel die, the six-layered nickel-alumina graded structure was fabricated considering 0%, 20%, 40%, 60%, 80% and 100% weight percentage of ceramic concentration for different layers. A hydraulic press was used for fabrication of the FGM layered structure and 30 ton compaction load was applied. Considering two-step sintering cycle, sintering was carried out at sintering temperature 1200 °C and sintering time 4 h. The sintered specimens were characterized using optical microscopy (OM), scanning electron microscopy (SEM) and hardness testing. It was observed that uniform particle distribution within the graded layers and smooth microstructural transition occurred between adjacent layers. It was also observed that the interface lines are obvious, less wavy, straight and parallel which confirms proper layer stacking process. On the other hand, from the SEM micrographs, the existence of microcracks and voids are identified in the alumina-rich layer and mostly around alumina particles

Reducing model bias in a deep learning classifier using domain adversarial neural networks in the MINERvA experiment

We present a simulation-based study using deep convolutional neural networks (DCNNs) to identify neutrino interaction vertices in the MINERvA passive targets region, and illustrate the application of domain adversarial neural networks (DANNs) in this context. DANNs are designed to be trained in one domain (simulated data) but tested in a second domain (physics data) and utilize unlabeled data from the second domain so that during training only features which are unable to discriminate between the domains are promoted. MINERvA is a neutrino-nucleus scattering experiment using the NuMI beamline at Fermilab. $A$-dependent cross sections are an important part of the physics program, and these measurements require vertex finding in complicated events. To illustrate the impact of the DANN we used a modified set of simulation in place of physics data during the training of the DANN and then used the label of the modified simulation during the evaluation of the DANN. We find that deep learning based methods offer significant advantages over our prior track-based reconstruction for the task of vertex finding, and that DANNs are able to improve the performance of deep networks by leveraging available unlabeled data and by mitigating network performance degradation rooted in biases in the physics models used for training.Comment: 41 page

First evidence of coherent K+K^{+} meson production in neutrino-nucleus scattering

Neutrino-induced charged-current coherent kaon production, $\nu_{\mu}A\rightarrow\mu^{-}K^{+}A$, is a rare, inelastic electroweak process that brings a $K^+$ on shell and leaves the target nucleus intact in its ground state. This process is significantly lower in rate than neutrino-induced charged-current coherent pion production, because of Cabibbo suppression and a kinematic suppression due to the larger kaon mass. We search for such events in the scintillator tracker of MINERvA by observing the final state $K^+$, $\mu^-$ and no other detector activity, and by using the kinematics of the final state particles to reconstruct the small momentum transfer to the nucleus, which is a model-independent characteristic of coherent scattering. We find the first experimental evidence for the process at $3\sigma$ significance.Comment: added ancillary file with information about the six kaon candidate

Measurement of Total and Differential Cross Sections of Neutrino and Antineutrino Coherent π±\pi^\pm Production on Carbon

Neutrino induced coherent charged pion production on nuclei, $\overline{\nu}_\mu A\to\mu^\pm\pi^\mp A$, is a rare inelastic interaction in which the four-momentum squared transfered to the nucleus is nearly zero, leaving it intact. We identify such events in the scintillator of MINERvA by reconstructing |t| from the final state pion and muon momenta and by removing events with evidence of energetic nuclear recoil or production of other final state particles. We measure the total neutrino and antineutrino cross sections as a function of neutrino energy between 2 and 20 GeV and measure flux integrated differential cross sections as a function of $Q^2$, $E_\pi$ and $\theta_\pi$. The $Q^2$ dependence and equality of the neutrino and anti-neutrino cross-sections at finite $Q^2$ provide a confirmation of Adler's PCAC hypothesis