An effective three-dimensional ordered mesoporous CuCo2O4 as electrocatalyst for Li-O2 batteries

Three-dimensional ordered mesoporous (3DOM) CuCo₂O₄ materials have been synthesized via a hard template and used as bifunctional electrocatalysts for rechargeable Li-O₂ batteries. The characterization of the catalyst by X-ray diffractometry and transmission electron microscopy confirms the formation of a single-phase, 3-dimensional, ordered mesoporous CuCo₂O₄ structure. The as-prepared CuCo₂O₄ nanoparticles possess a high specific surface area of 97.1 m² g^{- 1} and a spinel crystalline structure. Cyclic voltammetry demonstrates that mesoporous CuCo₂O₄ catalyst enhances the kinetics for either oxygen reduction reaction (ORR) or oxygen evolution reaction (OER). The Li-O₂ battery utilizing 3DOM CuCo₂O₄ shows a higher specific capacity of 7456 mAh g^{- 1} than that with pure Ketjen black (KB). Moreover, the CuCo₂O₄-based electrode enables much enhanced cyclability with a 610 mV smaller discharge-recharge voltage gap than that of the carbon-only cathode at a current rate of 100 mA g^{- 1}. Such excellent catalytic performance of CuCo₂O₄ could be associated with its larger surface area and 3D ordered mesoporous structure. The excellent electrochemical performances coupled with its facile and cost-effective way will render the 3D mesoporous CuCo₂O₄ nanostructures as attractive electrode materials for promising application in Li-O₂ batteries.

Towards a Resilience Metric Framework for Cyber-Physical Systems

Resilience is widely accepted as a desirable system property for cyber-physical systems. However, there are no metrics that can be used to measure the resilience of cyber-physical systems (CPS) while the multi-dimensional nature of performance in these systems is considered. In this work, we present first results towards a resilience metric framework. The key contributions of this framework are threefold: First, it allows to evaluate resilience with respect to different performance indicators that are of interest. Second, complexities that are relevant to the performance indicators of interest, can be intentionally abstracted. Third and final, it supports the identification of reasons for good or bad resilience to improve system design.