Copper-Doped NiOOH for the Electrocatalytic Conversion of Glycerol to Formate via the Inhibition of the Oxygen Evolution Reaction.

The combination of the electrocatalytic glycerol oxidation reaction (GOR) with the cathodic hydrogen evolution reaction serves to reduce the anodic overpotential, thereby facilitating the efficient production of hydrogen. However, the GOR is confined to a narrow potential range due to the competition of the oxygen evolution reaction (OER) at high potential. Therefore, it is necessary to develop a catalyst with a high Faraday efficiency of formate (FE) over a wide potential range.

Electron-rich SnO promote CO activation for stable electrocatalytic CO reduction.

Electrocatalytic CO reduction reaction (CORR) to formate offers a promising route for carbon neutralization, but its reactivity is largely compromised due to the competitive hydrogen evolution reaction (HER) accompanying the activation of CO at high potentials. Herein, we modulated the charge density around Sn atoms by introducing LaSnO into SnO, with the rich grain boundaries and fast electron transport of the heterostructure promoting CO reduction. Combined theoretical calculations and in situ electrochemical attenuated total reflection Fourier-transform infrared spectroscopy (ATR-FTIR) characterization revealed enhanced activation of CO and adsorption of *OCHO intermediates by the constructed electron-rich SnO.

Parallelizing Backpropagation Neural Network Using MapReduce and Cascading Model.

Artificial Neural Network (ANN) is a widely used algorithm in pattern recognition, classification, and prediction fields. Among a number of neural networks, backpropagation neural network (BPNN) has become the most famous one due to its remarkable function approximation ability. However, a standard BPNN frequently employs a large number of sum and sigmoid calculations, which may result in low efficiency in dealing with large volume of data.

MapReduce Based Parallel Neural Networks in Enabling Large Scale Machine Learning.

Artificial neural networks (ANNs) have been widely used in pattern recognition and classification applications. However, ANNs are notably slow in computation especially when the size of data is large. Nowadays, big data has received a momentum from both industry and academia.