Industrially producing HO consumes lots of energy and generates byproducts. For the first time, we demonstrate the non-energy-consuming, self-powered production of HO based on a Zn-air battery with oxygenated carbon electrocatalyst. The battery with power density of 360 W m at a operating voltage of 0.8 V exhibited high HO production rate of 5.93 mol m h. By tuning the ratio of the oxygen-containg groups, the origin of the high activity was investigated. Combining the DFT calculations, we found that C-O-C and -CHO contribute more to the HO production compared to other functional groups.
The increasing demand for portable and wearable electronics requires lightweight, thin, and highly flexible power sources, for example, flexible zinc‐air batteries (ZABs). The so‐far reported flexible ZAB devices mostly remain bulky, with a design consisting of two relatively thick substrates (e.g., carbon cloths and/or metal foams) and a gel electrolyte‐coated separator in between. Herein, an ultrathin (≈0.2 mm) solid‐state ZAB with high flexibility and performance is introduced by directly forming self‐standing active layers on each surface of an alkaline polymer membrane through an ink‐casting/hot‐pressing approach. A Fe/N‐doped 3D carbon with hierarchic pores and an interconnected network structure is used as cathode electrocatalyst, so that the backing gas‐diffusion layer (e.g., carbon cloth) can be abandoned. What is further, a microstructure‐modulating method to significantly increase the FeN4 active sites for oxygen reduction reaction is developed, thus significantly boosting the performance of the ZAB. The assembled solid‐state ZAB manifests remarkable peak power density of 250 mW cm−3 and high capacity of 150.4 mAh cm−3 at 8.3 mA cm−3, as well as excellent flexibility. The new design should provide valuable opportunity to the portable and wearable electronics.
Parallel reservoir simulators are now widely used with availability of super computers. Modern massively parallel supercomputers demonstrate great power for simulating large-scale reservoir models. However, improving scalability and efficiency for fully implicit methods on emerging parallel architectures is still challenging. In this paper, we present a robust discretization together with a parallel linear solver algorithm; and we explore the parallel implementation on the world's fastest supercomputer Tianhe-2.Starting with a general compositional model, we focus on the black oil model and developed Parallel eXtension Framework for parallelizing the serial simulator. A parallel preconditioner based on fast auxiliary space preconditioning (FASP) is applied to solve the Jacobian system arising from the fully implicit discretization. The parallel simulator was validated using large-scale black oil benchmark problems, for which parallel scalabilities were tested. Giant reservoir models with over 100 million grid blocks have been simulated within a few minutes, and test the strong scalability of AMG solver with 1 billion unknown. We also demonstrate the parallelization and acceleration using Intel Xeon Phi coprocessors. In the end, the efficiency of the parallel simulator is illustrated by a giant reservoir using up to 10,000 cores, for which the CPU and communication time are summarized for the linear and nonlinear algorithms.
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