Ammonia (NH3) is an indispensable feedstock for fertilizer production and one of the most ideal green hydrogen rich fuel. Electrochemical nitrate (NO3−) reduction reaction (NO3−RR) is being explored as a promising strategy for green to synthesize industrial‐scale NH3, which has nonetheless involved complex multi‐reaction process. This work presents a Pd‐doped Co3O4 nanoarray on titanium mesh (Pd‐Co3O4/TM) electrode for highly efficient and selective electrocatalytic NO3−RR to NH3 at low onset potential. The well‐designed Pd‐Co3O4/TM delivers a large NH3 yield of 745.6 µmol h−1 cm−2 and an extremely high Faradaic efficiency (FE) of 98.7% at −0.3 V with strong stability. These calculations further indicate that the doping Co3O4 with Pd improves the adsorption characteristic of Pd‐Co3O4 and optimizes the free energies for intermediates, thereby facilitating the kinetics of the reaction. Furthermore, assembling this catalyst in a Zn‐NO3− battery realizes a power density of 3.9 mW cm−2 and an excellent FE of 98.8% for NH3.
Ammonia (NH3) is necessary for industry and daily life. Nitrate (NO3-) existing in both surface water and underground water is harmful to the environment and human body. Electrochemical NO3- reduction...
Compared with the traditional actuator, the fluid momentum controller actuator based on magnetohydrodynamics (MHD) has some unique advantages and characteristics. In this paper, a method is proposed for the shape optimization of fluid momentum ring cross section. Based on the engineering situation, this article proposes a mathematical model of angular momentum that can be used for analytical calculations. Second, the two shapes obtained are unified and mathematically expressed in terms of maximum power and minimum resistance, respectively. Finally, the particle swarm algorithm is used to optimize the parameters of the proposed shape in combination with finite element method (FEM). Compared with the common rectangular section scheme, the attitude adjustment performance of fluid momentum ring can be effectively improved. Specifically, for the same area of cross section, the fluid momentum rings with the proposed shape provide the angular momentum values that exceed those of the rectangular shape by 14%-17% for the cases considered. This method avoids the huge computation of computational fluid dynamics and multidisciplinary topology optimization.
Knowledge Graph has been proven effective in modeling structured information and conceptual knowledge, especially in the medical domain. However, the lack of high-quality annotated corpora remains a crucial problem for advancing the research and applications on this task. In order to accelerate the research for domain-specific knowledge graphs in the medical domain, we introduce DiaKG, a high-quality Chinese dataset for Diabetes knowledge graph, which contains 22,050 entities and 6,890 relations in total. We implement recent typical methods for Named Entity Recognition and Relation Extraction as a benchmark to evaluate the proposed dataset thoroughly. Empirical results show that the DiaKG is challenging for most existing methods and further analysis is conducted to discuss future research direction for improvements. We hope the release of this dataset can assist the construction of diabetes knowledge graphs and facilitate AI-based applications.
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