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 ...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.展开更多
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 im...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.展开更多
Global optimization of fuel cells is a key approach to enhance performance and extend lifespan.Furthermore,the response surface method can provide accurate predictive results with minimal data.This study utilizes the ...Global optimization of fuel cells is a key approach to enhance performance and extend lifespan.Furthermore,the response surface method can provide accurate predictive results with minimal data.This study utilizes the response surface method alongside a two-dimensional agglomerate model to perform numerical simulations of asymmetric proton exchange membrane fuel cells,focusing on thickness and operating parameters.The study analyzes the interactions among parameters and aims to identify optimal values for maximum power density.The structural and operational parameter optimization models have been developed,with average errors of 2.28%and 0.29%,respectively,leading to produce a predictive model with an average error of less than 3%ultimately.The optimized power density increased by 57.4%,with inlet pressure identified as the most influential factor.The asymmetric design enhances gas transport in the porous media region.Among the structural parameters,cathode thickness has a greater impact;while among the operating parameters,pressure exerts the greatest impact on cell performance.The optimal temperature ranges from 333 K to 343 K,with a noticeable marginal effect.Higher relative humidity can enhance power density,and it's worth noting that cathode humidity is more sensitive to power density than anode humidity.A well-designed asymmetric configuration can enhance the water and thermal management of the fuel cell,leading to improved energy efficiency.展开更多
基金Major Program of the National Natural Science Foundation of China,No.41590842
文摘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.
基金financially supported by the National Key R&D Program of China (2022YFE0101300)the National Natural Science Foundation of China (52176203)。
文摘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.
基金supported by the National Natural Science Foundation of China(No.52266018)Xinjiang Tianshan Elite Program—Young Scientific and Technological Talents Project(Project No.2022TSYCCX0051)。
文摘Global optimization of fuel cells is a key approach to enhance performance and extend lifespan.Furthermore,the response surface method can provide accurate predictive results with minimal data.This study utilizes the response surface method alongside a two-dimensional agglomerate model to perform numerical simulations of asymmetric proton exchange membrane fuel cells,focusing on thickness and operating parameters.The study analyzes the interactions among parameters and aims to identify optimal values for maximum power density.The structural and operational parameter optimization models have been developed,with average errors of 2.28%and 0.29%,respectively,leading to produce a predictive model with an average error of less than 3%ultimately.The optimized power density increased by 57.4%,with inlet pressure identified as the most influential factor.The asymmetric design enhances gas transport in the porous media region.Among the structural parameters,cathode thickness has a greater impact;while among the operating parameters,pressure exerts the greatest impact on cell performance.The optimal temperature ranges from 333 K to 343 K,with a noticeable marginal effect.Higher relative humidity can enhance power density,and it's worth noting that cathode humidity is more sensitive to power density than anode humidity.A well-designed asymmetric configuration can enhance the water and thermal management of the fuel cell,leading to improved energy efficiency.
