Supercapacitors (SCs) have received a great deal of attention and play an important role for future self-powered devices, mainly owing to their higher power density. Among all types of electrical energy storage devices, electrochemical supercapacitors are considered to be the most promising because of their superior performance characteristics, including short charging time, high power density, safety, easy fabrication procedures, and long operational life. An SC consists of two foremost components, namely electrode materials, and electrolyte. The selection of appropriate electrode materials with rational nanostructured designs has resulted in improved electrochemical properties for high performance and has reduced the cost of SCs. In this review, we mainly spotlight the non-metallic oxide, especially metal chalcogenides (MX; X = S, Se) based nanostructured electrode materials for electrochemical SCs. Different non-metallic oxide materials are highlighted in various categories, such as transition metal sulfides and selenides materials. Finally, the designing strategy and future improvements on metal chalcogenide materials for the application of electrochemical SCs are also discussed.

The time constraint in the growth of ZnO nanostructures when using a hydrothermal method is of paramount importance in contemporary research, where a long fabrication time rots the very essence of the research on ZnO nanostructures. In this study, we present the facile and ultrafast growth of ZnO nanostructures in a domestic microwave oven within a pressurized environment in just a few minutes. This method is preferred for the conventional solution-based method because of the ultrafast supersaturation of zinc salts and the fabrication of high-quality nanostructures. The study of the effect of seed layer density, growth time, and the solution’s molar concentration on the morphology, alignment, density, and aspect ratio of ZnO nanorods (ZNRs) is explored. It is found in a microwave-assisted direct growth method that ~5 mins is the optimum time beyond which homogeneous nucleation supersedes heterogeneous nucleation, which results in the growth stoppage of ZNRs. To deal with this issue, we propound different methods such as microwave-assisted solution-replacement, preheating, and PEI-based growth methods, where growth stoppage is addressed and ZNRs with a high aspect ratio can be grown. Furthermore, high-quality ZnO nanoflowers and ZnO nanowalls are fabricated via ammonium hydroxide treatment in a very short time.

Over the last few decades, the development of the electronic nose (E-nose) for detection and quantification of dangerous and odorless gases, such as methane (CH4) and carbon monoxide (CO), using an array of SnO2 gas sensors has attracted considerable attention. This paper addresses sensor cross sensitivity by developing a classifier and estimator using an artificial neural network (ANN) and least squares regression (LSR), respectively. Initially, the ANN was implemented using a feedforward pattern recognition algorithm to learn the collective behavior of an array as the signature of a particular gas. In the second phase, the classified gas was quantified by minimizing the mean square error using LSR. The combined approach produced 98.7% recognition probability, with 95.5 and 94.4% estimated gas concentration accuracies for CH4 and CO, respectively. The classifier and estimator parameters were deployed in a remote microcontroller for the actualization of a wireless E-nose system.

Cloud computing offers the services to access, manipulate and configure data online over the web. The cloud term refers to an internet network which is remotely available and accessible at anytime from anywhere. Cloud computing is undoubtedly an innovation as the investment in the real and physical infrastructure is much greater than the cloud technology investment. The present work addresses the issue of power consumption done by cloud infrastructure. As there is a need for algorithms and techniques that can reduce energy consumption and schedule resource for the effectiveness of servers. Load balancing is also a significant part of cloud technology that enables the balanced distribution of load among multiple servers to fulfill users’ growing demand. The present work used various optimization algorithms such as particle swarm optimization (PSO), cat swarm optimization (CSO), BAT, cuckoo search algorithm (CSA) optimization algorithm and the whale optimization algorithm (WOA) for balancing the load, energy efficiency, and better resource scheduling to make an efficient cloud environment. In the case of seven servers and eight server’s settings, the results revealed that whale optimization algorithm outperformed other algorithms in terms of response time, energy consumption, execution time and throughput.

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