A pillar-layered Ni2P-Ni5P4-CoP array derived from a metal-organic framework as a bifunctional catalyst for efficient overall water splitting.

Interfacial engineering emerges as a potent strategy for regulating the catalytic reactivity of metal phosphides. Developing a facile and cost-effective method to construct bifunctional metal phosphides for highly efficient electrochemical overall water splitting remains an essential and challenging issue. Here, a multiphase transition metal phosphide is constructed through the direct phosphorization of a Ni-Co metal-organic framework grown on nickel foam (Ni-Co-MOF/NF), which is prepared by utilizing nickel foam as conductive substrate and nickel source. The resulting transition metal phosphide manifests a pillar-layered morphology, wherein CoP, Ni2P, and Ni5P4 nanoparticles are embedded within each carbon sheet and these carbon sheets assemble into a pillar-shaped structure on the nickel foam (Ni2P-Ni5P4-CoP-C/NF). The heterogeneous Ni2P-Ni5P4-CoP-C/NF with multiple interfaces serves as a highly efficient bifunctional electrocatalyst with overpotentials of -100 mV and 293 mV in the hydrogen evolution reaction and oxygen evolution reaction, respectively, at 50 mA cm-2 in alkaline media. This superior catalytic performance should mainly be ascribed to its enriched active centers and multiphase synergy. When directly applied for alkaline overall water splitting, the Ni2P-Ni5P4-CoP-C/NF couple demonstrates satisfactory activity (1.55 V @10 mA cm-2) along with sustained durability over 18 hours. This method brings fresh enlightenment to the economical and controllable preparation of multi-metal phosphides for energy conversion.

A water quality prediction model based on variational mode decomposition and the least squares support vector machine optimized by the sparrow search algorithm (VMD-SSA-LSSVM) of the Yangtze River, China.

Accurate and reliable water quality forecasting is of great significance for water resource optimization and management. This study focuses on the prediction of water quality parameters such as the dissolved oxygen (DO) in a river system. The accuracy of traditional water quality prediction methods is generally low, and the prediction results have serious autocorrelation. To overcome nonstationarity, randomness, and nonlinearity of the water quality parameter data, an improved least squares support vector machine (LSSVM) model was proposed to improve the model's performance at two gaging stations, namely Panzhihua and Jiujiang, in the Yangtze River, China. In addition, a hybrid model that recruits variational mode decomposition (VMD) to denoise the input data was adopted. A novel metaheuristic optimization algorithm, the sparrow search algorithm (SSA) was also implemented to compute the optimal parameter values for the LSSVM model. To validate the proposed hybrid model, standalone LSSVM, SSA-LSSVM, VMD-LSSVM, support vector regression (SVR), as well as back propagation neural network (BPNN) were considered as the benchmark models. The results indicated that the VMD-SSA-LSSVM model exhibited the best forecasting performance among all the peer models at Panzhihua station. Furthermore, the model forecasting results applied at Jiujiang were consistent with those at Panzhihua station. This result further verified the accuracy and stability of the VMD-SSA-LSSVM model. Thus, the proposed hybrid model was effective method for forecasting nonstationary and nonlinear water quality parameter series and can be recommended as a promising model for water quality parameter forecasting.