Freestanding, paper-like films of reduced graphene oxide (rGO) containing trace amounts of polymers are fabricated by an operationally simple, cost-effective, and environmentally friendly gel-film approach. The films, which can have a large area, display ultrahigh strengths and toughnesses as well as high electrical conductivities.
The emergence of superconductivity in 2D materials has attracted much attention and there has been rapid development in recent years because of their fruitful physical properties, such as high transition temperature (Tc), continuous phase transition, and enhanced parallel critical magnetic field (Bc). Tremendous efforts have been devoted to exploring different physical parameters to figure out the mechanisms behind the unexpected superconductivity phenomena, including adjusting the thickness of samples, fabricating various heterostructures, tuning the carrier density by electric field and chemical doping, and so on. Here, different types of 2D superconductivity with their unique characteristics are introduced, including the conventional Bardeen–Cooper–Schrieffer superconductivity in ultrathin films, high‐Tc superconductivity in Fe‐based and Cu‐based 2D superconductors, unconventional superconductivity in newly discovered twist‐angle bilayer graphene, superconductivity with enhanced Bc, and topological superconductivity. A perspective toward this field is then proposed based on academic knowledge from the recently reported literature. The aim is to provide researchers with a clear and comprehensive understanding about the newly developed 2D superconductivity and promote the development of this field much further.
Summary
The cloud infrastructures provide a suitable environment for the execution of large‐scale scientific workflow application. However, it raises new challenges to efficiently allocate resources for the workflow application and also to meet the user's quality of service requirements. In this paper, we propose an adaptive penalty function for the strict constraints compared with other genetic algorithms. Moreover, the coevolution approach is used to adjust the crossover and mutation probability, which is able to accelerate the convergence and prevent the prematurity. We also compare our algorithm with baselines such as Random, particle swarm optimization, Heterogeneous Earliest Finish Time, and genetic algorithm in a WorkflowSim simulator on 4 representative scientific workflows. The results show that it performs better than the other state‐of‐the‐art algorithms in the criterion of both the deadline‐constraint meeting probability and the total execution cost.
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