Donghua University

About: Donghua University is a education organization based out in Shanghai, China. It is known for research contribution in the topics: Fiber & Nanofiber. The organization has 21155 authors who have published 21841 publications receiving 393091 citations. The organization is also known as: Dōnghuá Dàxué & China Textile University.

Thin MoS2 nanosheets grafted MOFs-derived porous Co–N–C flakes grown on electrospun carbon nanofibers as self-supported bifunctional catalysts for overall water splitting

Abstract: Active and stable non-precious metal electrocatalysts are critical for the large-scale production of hydrogen/oxygen. Herein, a facile strategy for the in situ growth of MOFs combined with carbonization and subsequent solvothermal treatment for the rational design of thin MoS2 nanosheets grafted Co–N–C flakes (CoNC@MoS2) and grown on electrospun carbon nanofibers (CNFs) as bifunctional electrocatalysts for both hydrogen and oxygen evolution reactions (HER/OER) is reported. Binder-free CoNC@MoS2/CNF films exhibited unique hierarchical architectures with interconnected vine-like CNFs, which imparted favorable flexibility and satisfactory electrical conductivity to the self-supported electrocatalysts for electrochemical reactions. Due to the synergistic effect of the CoNC@MoS2 hybrid nanostructures and fast mass-transport properties of porous carbons, the resultant CoNC@MoS2/CNFs exhibited high catalytic activities and favorable stabilities for the HER and OER in a basic medium. When acting as electrocatalytic electrodes for overall water splitting, CoNC@MoS2/CNF films displayed a low overpotential of 1.62 V to generate a current density of 10 mA cm−2 with remarkable stability at different voltages for 200 000 s, and even outperformed Pt/C–RuO2 electrode in high current density water electrolysis. This study highlights the rational design of hybrid nanostructures based on MOFs and CNFs as efficient self-supported electrocatalysts, opening new possibilities for the fabrication of functional free-standing materials in energy chemistry.

Gain-Constrained Recursive Filtering With Stochastic Nonlinearities and Probabilistic Sensor Delays

Abstract: This paper is concerned with the gain-constrained recursive filtering problem for a class of time-varying nonlinear stochastic systems with probabilistic sensor delays and correlated noises. The stochastic nonlinearities are described by statistical means that cover the multiplicative stochastic disturbances as a special case. The phenomenon of probabilistic sensor delays is modeled by introducing a diagonal matrix composed of Bernoulli distributed random variables taking values of 1 or 0, which means that the sensors may experience randomly occurring delays with individual delay characteristics. The process noise is finite-step autocorrelated. The purpose of the addressed gain-constrained filtering problem is to design a filter such that, for all probabilistic sensor delays, stochastic nonlinearities, gain constraint as well as correlated noises, the cost function concerning the filtering error is minimized at each sampling instant, where the filter gain satisfies a certain equality constraint. A new recursive filtering algorithm is developed that ensures both the local optimality and the unbiasedness of the designed filter at each sampling instant which achieving the pre-specified filter gain constraint. A simulation example is provided to illustrate the effectiveness of the proposed filter design approach.