Geometric Engineering of Porous PtCu Nanotubes with Ultrahigh Methanol Oxidation and Oxygen Reduction Capability.

Platinum (Pt), as a commonly used electrocatalyst in direct methanol fuel cells (DMFCs), suffers from sluggish kinetics of both the methanol oxidation reaction (MOR) and oxygen reduction reaction (ORR). Geometric engineering has been proven effective for improving the MOR and ORR activities. Thus, by modulating the Pt precursor and poly(vinylpyrrolidone) (PVP) dosages, different porous PtCu nanotubes constructed by hollow nanospheres, solid alloy, and Pt-rich skinned nanoparticles, respectively, are successfully synthesized. Among them, the solid PtCu alloy nanoparticle coherent nanotubes exhibit the specific activity 9.42 times higher than Pt/C toward MOR, while the hollow PtCu alloy nanosphere coherent nanotubes show the specific activity 4.85 times higher than Pt/C toward ORR. The different Pt:Cu ratios of hollow nanospheres, solid alloy, and Pt-rich skinned nanoparticles cause the differences in electron transfer from Cu to Pt as well as electronic structures of Pt. As a result, the binding energies of intermediates can be regulated, leading to the enhancement in MOR and ORR.

The Application of Social Characteristic and L1 Optimization in the Error Correction for Network Coding in Wireless Sensor Networks.

One of the remarkable challenges about Wireless Sensor Networks (WSN) is how to transfer the collected data efficiently due to energy limitation of sensor nodes. Network coding will increase network throughput of WSN dramatically due to the broadcast nature of WSN. However, the network coding usually propagates a single original error over the whole network. Due to the special property of error propagation in network coding, most of error correction methods cannot correct more than C/2 corrupted errors where C is the max flow min cut of the network. To maximize the effectiveness of network coding applied in WSN, a new error-correcting mechanism to confront the propagated error is urgently needed. Based on the social network characteristic inherent in WSN and L1 optimization, we propose a novel scheme which successfully corrects more than C/2 corrupted errors. What is more, even if the error occurs on all the links of the network, our scheme also can correct errors successfully. With introducing a secret channel and a specially designed matrix which can trap some errors, we improve John and Yi's model so that it can correct the propagated errors in network coding which usually pollute exactly 100% of the received messages. Taking advantage of the social characteristic inherent in WSN, we propose a new distributed approach that establishes reputation-based trust among sensor nodes in order to identify the informative upstream sensor nodes. With referred theory of social networks, the informative relay nodes are selected and marked with high trust value. The two methods of L1 optimization and utilizing social characteristic coordinate with each other, and can correct the propagated error whose fraction is even exactly 100% in WSN where network coding is performed. The effectiveness of the error correction scheme is validated through simulation experiments.

A data-driven model for real-time water quality prediction and early warning by an integration method.

Due to increasingly serious deterioration of surface water quality, effective water quality prediction technique for real-time early warning is essential to guarantee the emergency response ability in advance for sustainable water management. In this study, an effective data-driven model for surface water quality prediction is developed to analyze the inherent water quality variation tendencies and provide real-time early warnings according to the historical observation data. The developed data-driven model is integrated by an improved genetic algorithm (IGA) for selecting optimal initial weight parameters of neural a network and a back-propagation neural network (BPNN) for adjusting appropriate connection architectures of neural network. First, improved genetic algorithm is used to optimize the reasonable initial weight parameters and prevent the developed model from selecting a local optimal result. Second, BPNN is applied to adjust appropriate connection architectures and identify the features of water quality variation. The developed model is then applied to forecast the surface water quality variations for real-time early warning in Ashi River, China, comparing with simple BPNN model. The prediction results demonstrate that the developed data-driven model can significantly improve the prediction performance both in prediction accuracy and reliability, and effectively provide real-time early warning for emergency response.