Amorphous hierarchical NiCo2O4–CoNiO2hybrids have been successfully fabricatedviaa facile one-pot hydrothermal route, followed by morphologic conversion into urchin-like structured NiCo2O4–CoNiO2nanorods and irregular-shaped hierarchical NiCo2O4–CoNiO2polyhedral nanocrystals through air-annealing treatment at 450 °C and 650 °C, respectively.

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Atomic-scale free volume holes have significant effects on the dielectric constant and breakdown strength of polymer composite films.

In this article, the self-supported power conditioning circuits are studied for a footstep energy harvester, which consists of a monolithic multilayer piezoelectric stack with a force amplification frame to extract electricity from human walking locomotion. Based on the synchronized switch harvesting on inductance (SSHI) technology, the power conditioning circuits are designed to optimize the power flow from the piezoelectric stack to the energy storage device under real-time human walking excitation instead of a simple sine waveform input, as reported in most literatures. The unique properties of human walking locomotion and multilayer piezoelectric stack both impose complications for circuit design. Three common interface circuits, for example, standard energy harvesting circuit, series-SSHI, and parallel-SSHI, are compared in terms of their output power to find the best candidate for the real-time-footstep energy harvester. Experimental results show that the use of parallel-SSHI circuit interface produces 74% more power than the standard energy harvesting counterpart, while the use of series-SSHI circuit demonstrates a similar performance in comparison to the standard energy harvesting interface. The reasons for such a huge efficiency improvement using the parallel-SSHI interface are detailed in this article.

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A detailed study of a set of combined acceleration methods is presented with the objective of accelerating the solid rocket motor grain burnback simulation based on the level set method. Relevant methods were improved by making use of unique characteristics of the grains, and graphical processing unit (GPU) parallelization is utilized to perform the computationally intensive operations. The presented flow traced the expansion of burning surfaces, and then Boolean operations were applied on the resulting surfaces to extract various geometric metrics. The initial signed distance field was built by an improved distance field generating method, and a highly optimized GPU kernel was used for estimating the gradient required by the level set method. An innovative Boolean operation method, thousands of times faster than ordinary ones, was ultimately proposed. Performance tests show that the overall speedup was close to 15 on desktop-class hardware, simulation results were proven to converge to analytical results, and the error boundary was 0.25%.

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