A confined Ni(0) nanocatalyst derived from NiAl-layered double hydroxide (NiAl-LDH), with ultrafine Ni nanoparticles implanted in the Ni x Al y O support, was first fabricated by the urea coprecipitation method using pine pollen as a biotemplate, together with subsequent calcination/ reduction process. This hollow biotemplate-assisted catalyst displayed high phenol-hydrogenation activity and stability, which can be ascribed to its higher surface area, better dispersibility, and ultrafine Ni particle size. The hollow morphology and confinement effect of lamellar NiAl-LDH can not only lead to better dispersion of the active Ni(0) species, but also strengthen the interaction between Ni(0) species and Ni x Al y O support. This protocol can overcome the shortcomings of conventional LDH-derived catalysts that lack specifically shaped morphology and hierarchical structure, inhibiting the aggregation of the Ni nanoparticles, hence resulting in its good activity and stability.
Despite the abundant research on energy-efficient rate scheduling polices in energy harvesting communication systems, few works have exploited data sharing among multiple applications to further enhance the energy utilization efficiency, considering that the harvested energy from environments is limited and unstable. In this paper, to overcome the energy shortage of wireless devices at transmitting data to a platform running multiple applications/requesters, we design rate scheduling policies to respond to data requests as soon as possible by encouraging data sharing among data requests and reducing the redundancy. We formulate the problem as a transmission completion time minimization problem under constraints of dynamical data requests and energy arrivals. We develop offline and online algorithms to solve this problem. For the offline setting, we discover the relationship between two problems: the completion time minimization problem and the energy consumption minimization problem with a given completion time. We first derive the optimal algorithm for the min-energy problem and then adopt it as a building block to compute the optimal solution for the min-completion-time problem. For the online setting without future information, we develop an event-driven online algorithm to complete the transmission as soon as possible. Simulation results validate the efficiency of the proposed algorithm.
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