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Abstract This paper describes the development of mixed B-site pyrochlore Y 2 MnRuO 7 electrocatalyst for oxygen evolution reaction (OER) in acidic media, a challenge for the development of low-temperature electrolyzer for green hydrogen production. Recently, several theories have been developed to understand the reaction mechanism for OER, though there is an  uncertainty in most of the cases, due to the complex surface structures. Several key factors such as lattice oxygen, defect, electronic structure, oxidation state, hydroxyl group and conductivity were identified and shown to be important to the OER activity. The contribution of each factor to the performance however is often not well understood, limiting their impact in guiding the design of OER electrocatalysts. In this work, we showed mixed B-site pyrochlore Y 2 MnRuO 7 catalyst exhibits 14 times higher turnover frequency (TOF) than RuO 2 while maintaining a low overpotential of ~ 300 mV for the entire testing period of 24 h in acidic electrolyte. X-ray photoelectron spectroscopy (XPS) analysis reveals that this B-site mixed pyrochlore Y 2 MnRuO 7 has a higher oxidation state of Ru than those of Y 2 Ru 2 O 7 , which could be crucial for improving OER performance as the broadened and loweredmore »Ru 4d band resulted from the B-site substitution by Mn is beneficial to the OER kinetics.« less

Smart resilience is the beneficial result of the collision course of the fields of data science and urban resilience to flooding. The objective of this study is to propose and demonstrate a smart flood resilience framework that leverages heterogeneous community-scale big data and infrastructure sensor data to enhance predictive risk monitoring and situational awareness. The smart flood resilience framework focuses on four core capabilities that could be augmented by the use of heterogeneous community-scale big data and analytics techniques: (1) predictive flood risk mapping; (2) automated rapid impact assessment; (3) predictive infrastructure failure prediction and monitoring; and (4) smart situational awareness capabilities. We demonstrate the components of these core capabilities of the smart flood resilience framework in the context of the 2017 Hurricane Harvey in Harris County, Texas. First, we present the use of flood sensors for the prediction of floodwater overflow in channel networks and inundation of co-located road networks. Second, we discuss the use of social media and machine learning techniques for assessing the impacts of floods on communities and sensing emotion signals to examine societal impacts. Third, we describe the use of high-resolution traffic data in network-theoretic models for nowcasting of flood propagation on road networksmore »and the disrupted access to critical facilities, such as hospitals. Fourth, we introduce how location-based and credit card transaction data were used in spatial analyses to proactively evaluate the recovery of communities and the impacts of floods on businesses. These analyses show that the significance of core capabilities of the smart flood resilience framework in helping emergency managers, city planners, public officials, responders, and volunteers to better cope with the impacts of catastrophic flooding events.

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