High entropy alloys(HEA)are frequently employed as catalysts in electrocatalytic hydrogen evolution.However,the traditional high entropy alloy synthesis methods are time-consuming,energy-intensive,and environmentally ...High entropy alloys(HEA)are frequently employed as catalysts in electrocatalytic hydrogen evolution.However,the traditional high entropy alloy synthesis methods are time-consuming,energy-intensive,and environmentally polluting,which limits their application in the hydrogen evolution reaction(HER).This study leveraged the capabilities of flash Joule heating(FJH)to synthesize carbon-supported high-entropy alloy sulfide nanoparticles(CC-S-HEA)on carbon cloth(CC)with good self-standing properties within 300 ms.The carbon thermal shock generated by the Joule heating could pyrolyze the sulfur source into gas,resulting in numerous pore structures and defects on CC,forming an S-doped carbon substrate(CC-S).Then the S atoms were used to stably anchor the metal atoms on CC-S to form high-density uniformly dispersed HEA particles.The electrochemical test results demonstrated that CC-S-HEA prepared at 60 V flash voltage had HER performance comparable to Pt/C.The density functional theory(DFT)calculation indicated that the S atoms on CC-S accelerated the electron transfer between the carbon substrate and HEA particles.Moreover,the unique electronic structure of CC-S-HEA was beneficial to H*adsorption and promoted catalytic kinetics.The simplicity and versatility of FJH synthesis are of great significance for optimizing the synthesis of HEA and improving the quality of HEA products,which provides a broad application prospect for the synthesis of nanocatalysts with efficient HER performance.展开更多
Data imputation is an essential pre-processing task for data governance,aimed at filling in incomplete data.However,conventional data imputation methods can only partly alleviate data incompleteness using isolated tab...Data imputation is an essential pre-processing task for data governance,aimed at filling in incomplete data.However,conventional data imputation methods can only partly alleviate data incompleteness using isolated tabular data,and they fail to achieve the best balance between accuracy and eficiency.In this paper,we present a novel visual analysis approach for data imputation.We develop a multi-party tabular data association strategy that uses intelligent algorithms to identify similar columns and establish column correlations across multiple tables.Then,we perform the initial imputation of incomplete data using correlated data entries from other tables.Additionally,we develop a visual analysis system to refine data imputation candidates.Our interactive system combines the multi-party data imputation approach with expert knowledge,allowing for a better understanding of the relational structure of the data.This significantly enhances the accuracy and eficiency of data imputation,thereby enhancing the quality of data governance and the intrinsic value of data assets.Experimental validation and user surveys demonstrate that this method supports users in verifying and judging the associated columns and similar rows using theirdomain knowledge.展开更多
To solve the greasiness and irritation risks brought about by organic sun-screening agents in sunscreen emulsions,in this work,a sunscreen O/W/Si multiple emulsion was prepared by two-step emulsification method,in whi...To solve the greasiness and irritation risks brought about by organic sun-screening agents in sunscreen emulsions,in this work,a sunscreen O/W/Si multiple emulsion was prepared by two-step emulsification method,in which the outer oil phase was silicone oil and the inner oil phase was solid lipid nanoparticles coated with organic sun-screening agent.Several influencing factors on the formation and stability of the emulsion were analyzed,including inorganic salts,the volume fraction of outer oil phase(silicone oil),and the dosage of W/O emulsifier.The in vitro sunscreen performance,water resistance and skin permeability of different types of sunscreen emulsions were further studied.The results showed that the sunscreen O/W/Si multiple emulsion containing 22.5%silicone oil,2.5%emulsifier and 0.2%NaCl had the best stability under the experimental conditions.The SPF value and water resistance of sunscreen O/W/Si multiple emulsion were slightly higher than those of sunscreen W/O emulsion,but significantly higher than those of sunscreen O/W emulsion.Compared with sunscreen W/O emulsion,the in vitro transdermal permeability of organic sun-screening agent in sunscreen O/W/Si multiple emulsion was reduced by approximately 60%,indicative of higher safety and good application prospect in sunscreen cosmetics.展开更多
The domain of cyber-physical-social(CPS)big data is generally defined as the set consisting of all the elements in its defined domain,including domains of data,objects,tasks,application scenarios,and subjects.Visual a...The domain of cyber-physical-social(CPS)big data is generally defined as the set consisting of all the elements in its defined domain,including domains of data,objects,tasks,application scenarios,and subjects.Visual analytics is an emerging human-in-the-loop big data analytics paradigm that can exploit human perception to enhance human cognitive efficiency.展开更多
Visualizing intrinsic structures of high-dimensional data is an essential task in data analysis.Over the past decades,a large number of methods have been proposed.Among all solutions,one promising way for enabling eff...Visualizing intrinsic structures of high-dimensional data is an essential task in data analysis.Over the past decades,a large number of methods have been proposed.Among all solutions,one promising way for enabling effective visual exploration is to construct a k-nearest neighbor(KNN)graph and visualize the graph in a low-dimensional space.Yet,state-of-the-art methods such as the LargeVis still suffer from two main problems when applied to large-scale data:(1)they may produce unappealing visualizations due to the non-convexity of the cost function;(2)visualizing the KNN graph is still time-consuming.In this work,we propose a novel visualization algorithm that leverages a multilevel representation to achieve a high-quality graph layout and employs a cluster-based approximation scheme to accelerate the KNN graph layout.Experiments on various large-scale datasets indicate that our approach achieves a speedup by a factor of five for KNN graph visualization compared to LargeVis and yields aesthetically pleasing visualization results.展开更多
基金supported by Key Research and Development Project of Xuzhou City(No.KC21287)the National Natural Science Foundation of China(No.51974307).
文摘High entropy alloys(HEA)are frequently employed as catalysts in electrocatalytic hydrogen evolution.However,the traditional high entropy alloy synthesis methods are time-consuming,energy-intensive,and environmentally polluting,which limits their application in the hydrogen evolution reaction(HER).This study leveraged the capabilities of flash Joule heating(FJH)to synthesize carbon-supported high-entropy alloy sulfide nanoparticles(CC-S-HEA)on carbon cloth(CC)with good self-standing properties within 300 ms.The carbon thermal shock generated by the Joule heating could pyrolyze the sulfur source into gas,resulting in numerous pore structures and defects on CC,forming an S-doped carbon substrate(CC-S).Then the S atoms were used to stably anchor the metal atoms on CC-S to form high-density uniformly dispersed HEA particles.The electrochemical test results demonstrated that CC-S-HEA prepared at 60 V flash voltage had HER performance comparable to Pt/C.The density functional theory(DFT)calculation indicated that the S atoms on CC-S accelerated the electron transfer between the carbon substrate and HEA particles.Moreover,the unique electronic structure of CC-S-HEA was beneficial to H*adsorption and promoted catalytic kinetics.The simplicity and versatility of FJH synthesis are of great significance for optimizing the synthesis of HEA and improving the quality of HEA products,which provides a broad application prospect for the synthesis of nanocatalysts with efficient HER performance.
基金Project supported by the Key R&D"Pioneer"Tackling Plan Program of Zhejiang Province,China(No.2023C01119)the"Ten Thousand Talents Plan"Science and Technology Innovation Leading Talent Program of Zhejiang Province,China(No.2022R52044)+1 种基金the Major Standardization Pilot Projects for the Digital Economy(Digital Trade Sector)of Zhejiang Province,China(No.SJ-Bz/2023053)the National Natural Science Foundationof China(No.62132017)。
文摘Data imputation is an essential pre-processing task for data governance,aimed at filling in incomplete data.However,conventional data imputation methods can only partly alleviate data incompleteness using isolated tabular data,and they fail to achieve the best balance between accuracy and eficiency.In this paper,we present a novel visual analysis approach for data imputation.We develop a multi-party tabular data association strategy that uses intelligent algorithms to identify similar columns and establish column correlations across multiple tables.Then,we perform the initial imputation of incomplete data using correlated data entries from other tables.Additionally,we develop a visual analysis system to refine data imputation candidates.Our interactive system combines the multi-party data imputation approach with expert knowledge,allowing for a better understanding of the relational structure of the data.This significantly enhances the accuracy and eficiency of data imputation,thereby enhancing the quality of data governance and the intrinsic value of data assets.Experimental validation and user surveys demonstrate that this method supports users in verifying and judging the associated columns and similar rows using theirdomain knowledge.
文摘To solve the greasiness and irritation risks brought about by organic sun-screening agents in sunscreen emulsions,in this work,a sunscreen O/W/Si multiple emulsion was prepared by two-step emulsification method,in which the outer oil phase was silicone oil and the inner oil phase was solid lipid nanoparticles coated with organic sun-screening agent.Several influencing factors on the formation and stability of the emulsion were analyzed,including inorganic salts,the volume fraction of outer oil phase(silicone oil),and the dosage of W/O emulsifier.The in vitro sunscreen performance,water resistance and skin permeability of different types of sunscreen emulsions were further studied.The results showed that the sunscreen O/W/Si multiple emulsion containing 22.5%silicone oil,2.5%emulsifier and 0.2%NaCl had the best stability under the experimental conditions.The SPF value and water resistance of sunscreen O/W/Si multiple emulsion were slightly higher than those of sunscreen W/O emulsion,but significantly higher than those of sunscreen O/W emulsion.Compared with sunscreen W/O emulsion,the in vitro transdermal permeability of organic sun-screening agent in sunscreen O/W/Si multiple emulsion was reduced by approximately 60%,indicative of higher safety and good application prospect in sunscreen cosmetics.
基金Project supported by the National Natural Science Foundation of China(No.62132017)。
文摘The domain of cyber-physical-social(CPS)big data is generally defined as the set consisting of all the elements in its defined domain,including domains of data,objects,tasks,application scenarios,and subjects.Visual analytics is an emerging human-in-the-loop big data analytics paradigm that can exploit human perception to enhance human cognitive efficiency.
文摘Visualizing intrinsic structures of high-dimensional data is an essential task in data analysis.Over the past decades,a large number of methods have been proposed.Among all solutions,one promising way for enabling effective visual exploration is to construct a k-nearest neighbor(KNN)graph and visualize the graph in a low-dimensional space.Yet,state-of-the-art methods such as the LargeVis still suffer from two main problems when applied to large-scale data:(1)they may produce unappealing visualizations due to the non-convexity of the cost function;(2)visualizing the KNN graph is still time-consuming.In this work,we propose a novel visualization algorithm that leverages a multilevel representation to achieve a high-quality graph layout and employs a cluster-based approximation scheme to accelerate the KNN graph layout.Experiments on various large-scale datasets indicate that our approach achieves a speedup by a factor of five for KNN graph visualization compared to LargeVis and yields aesthetically pleasing visualization results.
