Synthesis of $$\hbox {Ag}_{2}\hbox {Se}$$–graphene–$$\hbox {TiO}_{2} $$TiO2 nanocomposite and analysis of photocatalytic activity of $$\hbox {CO}_{2}$$CO2 reduction to $$\hbox {CH}_{3}\hbox {OH}$$CH3OH

The present work deals with the development of a new ternary composite, \(\hbox {Ag}_{2}\hbox {Se}\)–\(\hbox {G}\)–\(\hbox {TiO}_{2}\), using ultrasonic techniques as well as X-ray diffraction (XRD), scanning electron microscopy (SEM), high transmission electron microscopy (HTEM), X-ray photoelectron spectroscopy (XPS), Raman spectroscopy and UV–Vis diffuse reflectance spectra (DRS) analyses. The photocatalytic potential of nanocomposites is examined for \(\hbox {CO}_{2}\) reduction to methanol under ultraviolet (UV) and visible light irradiation. \(\hbox {Ag}_{2}\hbox {Se}\)–\(\hbox {TiO}_{2}\) with an optimum loading graphene of 10 wt% exhibited the maximum photoactivity, obtaining a total \(\hbox {CH}_{3}\hbox {OH}\) yield of 3.52 \(\upmu \hbox {mol}\,\hbox {g}^{-1}\,\hbox {h}^{-1}\) after 48 h. This outstanding photoreduction activity is due to the positive synergistic relation between \(\hbox {Ag}_{2}\hbox {Se}\) and graphene components in our heterogeneous system.     

Design,modeling, and analysis of a high performance piezoelectric energy harvester for intelligent tires

Basic parameters affecting vehicle safety and performance such as pressure, temperature, friction coefficient, and contact‐patch dimensions are measured in intelligent tires via sensors that require electric power for operation and wireless communication to be synchronized to the vehicle monitoring and control system. Piezoelectric energy harvesters (PEHs) can extract a fraction of energy that is wasted as a result of deflection during rolling of tires, and this extracted energy can be used to power up sensors embedded in intelligent tires. A new design of PEH inspired from Cymbal PEHs is introduced, and its performance is evaluated in this paper. Cymbal PEHs are proven to be useful in vibration energy harvesting, and in this paper, for the first time, the modified shape of Cymbal energy harvester is used as strain‐based energy harvester for the tire application. The shape of the harvester is adjusted in a way that it can be safely embedded on the inner surface of tires. In addition to the high performance, ease of manufacturing is another advantage of this new design. A multiphysics model is developed and validated to determine the output voltage, power, and energy of the designed PEH. The modeling results indicated that the maximum output voltage, the maximum electric power, and the accumulated harvested energy are about 3.5 V, 2.8 mW, and 24 mJ/rev, respectively, which are sufficient to power two sensors. In addition, the possibility is shown to supply power to five sensors by increase in piezoelectric material thickness. The effect of rolling tire temperature on the performance of the proposed PEH is also studied.     

Harris hawks optimization: a comprehensive review of recent variants and applications

Neural Computing and Applications - Harris hawks optimizer (HHO) has received widespread attention among researchers in terms of the performance, quality of results, and its acceptable convergence...     

Microstructure Evolution of Extruded Mg-6Gd Alloy Under 175 ℃ and 150 MPa

The tensile creep behavior of extruded Mg-6 Gd alloy,having the tensile yield strength of～ 110 MPa at 175 ℃,has been investigated under 175 ℃ and 150 MPa. In this study, the extruded Mg-6 Gd sample exhibits the total tensile strain of ～10.5% after the creep time of 1100 h,and the fast plastic strain of ～4.6% at the beginning of the creep test. The microstructure result suggests that the dislocation deformation is the main deformation mode during creep, and the grains with orientation close to(0001) II ED disappear after creep. The creep process containing a low creep strain has no effective promotion for the precipitation compared with the aging process without strain. The origination of creep crack is related to the formation of precipitate-free zone during creep. The work offers an important implication to research the microstructure evolution under an applied stress in a weak aging response Mg alloy.     

Remarkable ionic conductivity and catalytic activity in ceramic nanocomposite fuel cells

Although ceramic nanocomposite fuel cells (CNFCs) have attracted the attention of the fuel cell community due to their low operating temperature (<600 °C), often the performance of the cells is limited due to the low ionic conductivity of the electrolyte and the sluggish reaction kinetics at the electrodes. This results in high ohmic and charge transfer losses in the cell performance. Here we report nanocomposite electrolyte (GDC-NLC) and electrodes (NiO-GDC-NLC and LSCF-GDC-NLC as anode and cathode respectively) with enhanced ionic conductivity and catalytic activity respectively, which significantly improve the ionic transport in the electrolyte layer (ohmic losses ≈ 0.23 Ω cm²) and the reaction kinetics at the electrodes (polarization losses ≈ 0.63 Ω cm²). Microstructural and phase changes in the materials were characterized with X-ray diffraction, scanning electron microscopy, and differential scanning calorimetry to understand the mechanisms in the cells. Our button fuel cell produced an outstanding performance of 1.02 W/cm² at 550 °C.     

Optimal Path Planning for Two Unmanned Aerial Vehicles in DRSS Localization

International Journal of Control, Automation and Systems - Optimal path planning for three or more unmanned aerial vehicles (UAVs) in radio source localization has been studied extensively; but...     

Cognitive and affective causes of interest and liking.

In this study, I investigated some of the cognitive and affective causes of interest and liking. In Experiment 1, 240 undergraduates read stories with endings that varied in the degree of surprise, outcome valence (i.e., goodness or badness of outcome), and incongruity resolution. The results did not support the hypothesis that degree of surprise per se causes interest (Schank, 1979). Instead, as suggested by Kintsch (1980), subjects rated high-surprise story endings as more interesting than low-surprise story endings for those conditions in which the postsurprise incongruity was resolved (p?p?     

Periodic unsteady flows of a non-Newtonian fluid

Summary Exact analytic solutions for the flow of non-Newtonian fluid generated by periodic oscillations of a rigid plate are discussed. Some interesting flows caused by certain special oscillations are also obtained.     

Co‐doped ZnO thin films grown by pulsed electron beam ablation as model nano‐catalysts in fischer‐tropsch synthesis

A single‐step deposition of cobalt‐doped zinc oxide (Co‐ZnO) thin film nano‐composites on three different crystalline substrates, viz., Al₂O₃ (c‐sapphire), silicon (100) (Si), and SiO₂ (quartz) is reported, using pulsed electron beam ablation (PEBA). The results indicate that the type of substrate has no effect on Co‐ZnO films stoichiometry, morphology, microstructure, and film thickness. The findings show the presence of hexagonal close‐packed metallic Co whose content increases in the films deposited on Al₂O₃ and Si substrates relatively to SiO₂ substrate. The potential of the films as model nano‐catalysts has been evaluated in the context of the Fischer‐Tropsch (FT) process. Fuel fractions, which have been observed in FT liquid products, are rich in diesel and waxes. Specifically, Co‐ZnO/Al₂O₃ nano‐catalyst shows a selectivity of ～4%, 31%, and 65% towards gasoline, diesel, and waxes, respectively, while Co‐ZnO/SiO₂ nano‐catalyst shows a selectivity of ～12%, 51%, and 37%, for gasoline, diesel, and waxes, respectively. © 2018 American Institute of Chemical Engineers AIChE J, 64: 3332–3340, 2018     

An efficient deep learning technique for facial emotion recognition

Emotion recognition from facial images is considered as a challenging task due to the varying nature of facial expressions. The prior studies on emotion classification from facial images using deep learning models have focused on emotion recognition from facial images but face the issue of performance degradation due to poor selection of layers in the convolutional neural network model.To address this issue, we propose an efficient deep learning technique using a convolutional neural network model for classifying emotions from facial images and detecting age and gender from the facial expressions efficiently. Experimental results show that the proposed model outperformed baseline works by achieving an accuracy of 95.65% for emotion recognition, 98.5% for age recognition, and 99.14% for gender recognition.