Anti-Fibrinopeptide A polyclonal antibody

Metal-mediated exfoliation has been demonstrated as a promising approach for obtaining large-area flakes of two-dimensional (2D) materials to fabricate prototypical nanoelectronics. However, several processing challenges related to organic contamination at the interface of a 2D material and gate oxide must be overcome to realize robust devices with high yields. Here, we demonstrate an optimized process to realize high-performance field-effect transistor (FET) arrays from large-area (similar to 5000 mu m(2)), monolayer MoS2 with a yield of 85%. A central element of this process is an exposed material forming gas anneal (EM-FGA) that results in uniform FET performance metrics (i.e., field-effect mobilities, threshold voltages, and contact performance). Complementary analytical measurements show that the EM-FGA process reduces deleterious channel doping effects by decreasing organic contamination while also reducing the prevalence of insulating molybdenum oxide, effectively improving the MoS2-gate oxide interface. The uniform FET performance metrics and high device yield achieved by applying the EM-FGA technique on large-area 2D material flakes will help advance the fabrication of complex 2D nanoelectronic devices and demonstrate the need for improved engineering of the 2D material-gate oxide interface.

Reducing energy consumption in large-scale computing facilities has become a major concern in recent years. The large number of computing nodes, resources heterogeneity and diversity of application requirements are factors that turn the scheduling into an NP-Hard problem. Evolutionary algorithms have proved to be effective for scheduling applications. In this paper, we present a novel approach combining particle swarm optimization and a genetic algorithm to solving the resource matching and scheduling of parallel applications in Federated cluster environments. The proposed hybrid meta-heuristic, referred to as MPSO-FGA, not only minimizes the overall energy consumption but also the makespan for a whole workload. The experimental results show the superiority of evolutionary algorithms over basic heuristics. The hybrid meta-heuristic is able to obtain similar results to a genetic algorithm in terms of energy consumption and makespan but reducing the time for scheduling decisions by two orders of magnitude.