Electrospun zeolitic imidazolate framework‐derived nitrogen‐doped carbon nanofibers with high performance for lithium‐sulfur batteries

Three‐dimensional (3D) nitrogen‐doped carbon nanofibers (N‐CNFs) which were originating from nitrogen‐containing zeolitic imidazolate framework‐8 (ZIF‐8) were obtained by a combined electrospinning/carbonization technique. The pores uniformly distributed in N‐CNFs result in the improvement of electrical conductivity, increasing of BET surface area (142.82 m² g^?1), and high porosity. The as‐synthesized 3D free‐standing N‐CNFs membrane was applied as the current collector and binder free containing Li₂S₆ catholyte for lithium‐sulfur batteries. As a novel composite cathode, the free‐standing N‐CNFs/Li₂S₆ membrane shows more stable electrochemical behavior than the CNFs/Li₂S₆ membrane, exhibiting a high first‐cycle discharge specific capacity of 1175 mAh g^?1at 0.1 C and keeping discharge specific capacity of 702 mAh g^?1 at higher rate. More importantly, as the sulfur mass in cathodes was increased at 7.11 mg, the N‐CNFs/Li₂S₆ membrane delivered 467 mAh g^?1after 150 cycles at 0.2 C. The excellent electrochemical properties of N‐CNFs/Li₂S₆ membrane can be ascribed to synergistic effects of high porosity and nitrogen‐doping in N‐CNFs from carbonized ZIF‐8, illustrating collective effects of physisorption and chemisorption for lithium polysulfides in discharge‐charge processes.     

Ensemble of Surrogates for Dual Response Surface Modeling in Robust Parameter Design

The robust parameter design of industrial processes and products on the basis of the concept of building quality into a design has attracted much attention from researchers and practitioners for many years, and several methods have been studied in the research community. Dual response surface methodology is one of the most commonly used approaches for simultaneously optimizing the mean and the variance of response in quality engineering. Nevertheless, when the relationship between influential input factors and output quality characteristics of a process is very complex (e.g. highly nonlinear and noisy), traditional approaches have their limitations. In this article, we introduced support vector regression, kriging model, and radial basis function, which are commonly used in computer experiments, into robust parameter design, and especially introduced a new strategy that builds the dual response surface using the ensemble of surrogates, which can provide a more robust approximation model. We demonstrated the advantages of kriging, support vector regression, radial basis function, and the ensemble of surrogates by reinvestigating the dual response approach on the basis of parametric, nonparametric, and semiparametric approaches, and a simulation experiment is studied. The results show that our presented models can achieve more desirable results than parametric, nonparametric, and semiparametric approaches in terms of fitting and predictive accuracy, and the optimal operating conditions recommended by our presented models are similar to those recommended in literature, which indicates the validation of our presented models. Copyright © 2012 John Wiley & Sons, Ltd.