Fast design of catalyst layer with optimal electrical-thermal-water performance for proton exchange membrane fuel cells

The catalyst layer(CL)is the core component in determining the electrical-thermal-water performance and cost of proton exchange membrane fuel cell(PEMFC).Systemic analysis and rapid prediction tools are required to improve the design efficiency of CL.In this study,a 3D multi-phase model integrated with the multi-level agglomerate model for CL is developed to describe the heat and mass transfer processes inside PEMFC.Moreover,a research framework combining the response surface method(RSM)and artificial neural network(ANN)model is proposed to conduct a quantitative analysis,and further a rapid and accurate prediction.With the help of this research framework,the effects of CL composition on the electrical-thermal-water performance of PEMFC are investigated.The results show that the mass of platinum,the mass of carbon,and the volume fraction of dry ionomer has a significant impact on the electrical-thermal-water performance.At the selected points,the sensitivity of the decision variables is ranked:volume fraction of dry ionomer>mass of platinum>mass of carbon>agglomerate radius.In particular,the sensitivity of the volume fraction of dry ionomer is over 50%at these points.Besides,the comparison results show that the ANN model could implement a more rapid and accurate prediction than the RSM model based on the same sample set.This in-depth study is beneficial to provide feasible guidance for high-performance CL design.

An analysis of the differentiation rules and influencing factors of venture capital in Beijing-Tianjin-Hebei urban agglomeration

Under China's innovation-driven development strategy, venture capital has become an important driving force in urban agglomeration integration and collaborative innovation. This paper uses social network analysis to analyze spatiotemporal differences of venture capital in the Beijing-Tianjin-Hebei urban agglomeration for the period 2005–2015. A gravity model and panel data regression model are used to reveal the influencing factors on spatiotemporal differences in venture capital in the region. This study finds that there is a certain cyclical fluctuation and uneven differentiation in the venture capital network in the Beijing-Tianjin-Hebei urban agglomeration in terms of total investment, and that the three centers of venture capital(Beijing, Shijiazhuang and Tangshan) have a stimulatory effect on surrounding cities; flows of venture capital between cities display certain networking rules, but they are slow to develop and strongly centripetal; there is a strong positive correlation between levels of information infrastructure development and economic development and venture capital investment; and places with relatively underdeveloped financial environments and service industries are less able to apply the fruits of innovation and entrepreneurship and to attract funds. This study can act as a reference for the Beijing-Tianjin-Hebei urban agglomeration in building a world-class super urban agglomeration with the best innovation capabilities in China.