Lithium-sulfur(Li-S)batteries,although a promising candidate of next-generation energy storage devices,are hindered by some bottlenecks in their roadmap toward commercialization.The key challenges include solving the ...Lithium-sulfur(Li-S)batteries,although a promising candidate of next-generation energy storage devices,are hindered by some bottlenecks in their roadmap toward commercialization.The key challenges include solving the issues such as low utilization of active materials,poor cyclic stability,poor rate performance,and unsatisfactory Coulombic efficiency due to the inherent poor electrical and ionic conductivity of sulfur and its discharged products(e.g.,Li2S2 and Li_(2)S),dissolution and migration of polysulfide ions in the electrolyte,unstable solid electrolyte interphase and dendritic growth on an odes,and volume change in both cathodes and anodes.Owing to the high specific surface area,pore volume,low density,good chemical stability,and particularly multimodal pore sizes,hierarchical porous carbon(HPC)mate rials have received considerable attention for circumventing the above pro blems in Li-S batteries.Herein,recent progress made in the synthetic methods and deployment of HPC materials for various components including sulfur cathodes,separators and interlayers,and lithium anodes in Li-S batteries is presented and summarized.More importantly,the correlation between the structures(pore volume,specific surface area,degree of pores,and heteroatom-doping)of HPC and the electrochemical performances of Li-S batteries is elaborated.Finally,a discussion on the challenges and future perspectives associated with HPCs for Li-S batteries is provided.展开更多
The paper deals with cluster analysis and comparison of clustering methods. Cluster analysis belongs to multivariate statistical methods. Cluster analysis is defined as general logical technique, procedure, which allo...The paper deals with cluster analysis and comparison of clustering methods. Cluster analysis belongs to multivariate statistical methods. Cluster analysis is defined as general logical technique, procedure, which allows clustering variable objects into groups-clusters on the basis of similarity or dissimilarity. Cluster analysis involves computational procedures, of which purpose is to reduce a set of data on several relatively homogenous groups-clusters, while the condition of reduction is maximal and simultaneously minimal similarity of clusters. Similarity of objects is studied by the degree of similarity (correlation coefficient and association coefficient) or the degree of dissimilarity-degree of distance (distance coefficient). Methods of cluster analysis are on the basis of clustering classified as hierarchical or non-hierarchical methods.展开更多
The investment levels in electricity production capacity from variable Renewable Energy Sources have substantially grown in Brazil over the last decades,following the worldwide-seeking-goal of a carbon-neutral economy...The investment levels in electricity production capacity from variable Renewable Energy Sources have substantially grown in Brazil over the last decades,following the worldwide-seeking-goal of a carbon-neutral economy and the country’s incentives in diversifying its generation mix.From a long-term perspective,the current non-storable capability of renewable energy sources requires an adequate uncertainty characterization over the years.In this context,the main objective of this work is to provide a thorough descriptive analytics of the time-linked hourly-based daily dynamics of wind speed and solar irradiance in the main resourceful regions of Brazil.Leveraging on unsupervised Machine Learning methods,we focus on identifying similar days over the years,Representative Days,that can depict the fundamental underlying behaviour of each source.The analysis is based on a historical dataset of different sites with the highest potential and installed capacity of each source spread over the country:three in the Northeast and one in the South Regions,for wind speed;and three in the Northeast and one in the Southeast Regions,for solar irradiance.We use two Partitioning Methods(𝐾-Means and𝐾-Medoids),the Hierarchical Ward’s Method,and a Model-Based Method(Self-Organizing Maps).We identified that wind speed and solar irradiance can be effectively represented,respectively,by only two representative days,and two or three days,depending on the region and method(segments data with respect to the intensity of each source).Analysis with higher Representative Days highlighted important hidden patterns such as different wind speed modulations and solar irradiance peak-hours along the days.展开更多
The problem of taking a set of data and separating it into subgroups where the elements of each subgroup are more similar to each other than they are to elements not in the subgroup has been extensively studied throug...The problem of taking a set of data and separating it into subgroups where the elements of each subgroup are more similar to each other than they are to elements not in the subgroup has been extensively studied through the statistical method of cluster analysis. In this paper we want to discuss the application of this method to the field of education: particularly, we want to present the use of cluster analysis to separate students into groups that can be recognized and characterized by common traits in their answers to a questionnaire, without any prior knowledge of what form those groups would take (unsupervised classification). We start from a detailed study of the data processing needed by cluster analysis. Then two methods commonly used in cluster analysis are before described only from a theoretical point a view and after in the Section 4 through an example of application to data coming from an open-ended questionnaire administered to a sample of university students. In particular we describe and criticize the variables and parameters used to show the results of the cluster analysis methods.展开更多
基金Yinyu Xiang is very grateful to the China Scholarship Council(CSC:No.201806950083)for his PhD scholarship。
文摘Lithium-sulfur(Li-S)batteries,although a promising candidate of next-generation energy storage devices,are hindered by some bottlenecks in their roadmap toward commercialization.The key challenges include solving the issues such as low utilization of active materials,poor cyclic stability,poor rate performance,and unsatisfactory Coulombic efficiency due to the inherent poor electrical and ionic conductivity of sulfur and its discharged products(e.g.,Li2S2 and Li_(2)S),dissolution and migration of polysulfide ions in the electrolyte,unstable solid electrolyte interphase and dendritic growth on an odes,and volume change in both cathodes and anodes.Owing to the high specific surface area,pore volume,low density,good chemical stability,and particularly multimodal pore sizes,hierarchical porous carbon(HPC)mate rials have received considerable attention for circumventing the above pro blems in Li-S batteries.Herein,recent progress made in the synthetic methods and deployment of HPC materials for various components including sulfur cathodes,separators and interlayers,and lithium anodes in Li-S batteries is presented and summarized.More importantly,the correlation between the structures(pore volume,specific surface area,degree of pores,and heteroatom-doping)of HPC and the electrochemical performances of Li-S batteries is elaborated.Finally,a discussion on the challenges and future perspectives associated with HPCs for Li-S batteries is provided.
文摘The paper deals with cluster analysis and comparison of clustering methods. Cluster analysis belongs to multivariate statistical methods. Cluster analysis is defined as general logical technique, procedure, which allows clustering variable objects into groups-clusters on the basis of similarity or dissimilarity. Cluster analysis involves computational procedures, of which purpose is to reduce a set of data on several relatively homogenous groups-clusters, while the condition of reduction is maximal and simultaneously minimal similarity of clusters. Similarity of objects is studied by the degree of similarity (correlation coefficient and association coefficient) or the degree of dissimilarity-degree of distance (distance coefficient). Methods of cluster analysis are on the basis of clustering classified as hierarchical or non-hierarchical methods.
文摘The investment levels in electricity production capacity from variable Renewable Energy Sources have substantially grown in Brazil over the last decades,following the worldwide-seeking-goal of a carbon-neutral economy and the country’s incentives in diversifying its generation mix.From a long-term perspective,the current non-storable capability of renewable energy sources requires an adequate uncertainty characterization over the years.In this context,the main objective of this work is to provide a thorough descriptive analytics of the time-linked hourly-based daily dynamics of wind speed and solar irradiance in the main resourceful regions of Brazil.Leveraging on unsupervised Machine Learning methods,we focus on identifying similar days over the years,Representative Days,that can depict the fundamental underlying behaviour of each source.The analysis is based on a historical dataset of different sites with the highest potential and installed capacity of each source spread over the country:three in the Northeast and one in the South Regions,for wind speed;and three in the Northeast and one in the Southeast Regions,for solar irradiance.We use two Partitioning Methods(𝐾-Means and𝐾-Medoids),the Hierarchical Ward’s Method,and a Model-Based Method(Self-Organizing Maps).We identified that wind speed and solar irradiance can be effectively represented,respectively,by only two representative days,and two or three days,depending on the region and method(segments data with respect to the intensity of each source).Analysis with higher Representative Days highlighted important hidden patterns such as different wind speed modulations and solar irradiance peak-hours along the days.
文摘The problem of taking a set of data and separating it into subgroups where the elements of each subgroup are more similar to each other than they are to elements not in the subgroup has been extensively studied through the statistical method of cluster analysis. In this paper we want to discuss the application of this method to the field of education: particularly, we want to present the use of cluster analysis to separate students into groups that can be recognized and characterized by common traits in their answers to a questionnaire, without any prior knowledge of what form those groups would take (unsupervised classification). We start from a detailed study of the data processing needed by cluster analysis. Then two methods commonly used in cluster analysis are before described only from a theoretical point a view and after in the Section 4 through an example of application to data coming from an open-ended questionnaire administered to a sample of university students. In particular we describe and criticize the variables and parameters used to show the results of the cluster analysis methods.
