Efficient,safe,and reliable energy output from high-energy-density lithium metal batteries(LMBs)at all climates is crucial for portable electronic devices operating in complex environments.The performance of correspon...Efficient,safe,and reliable energy output from high-energy-density lithium metal batteries(LMBs)at all climates is crucial for portable electronic devices operating in complex environments.The performance of corresponding cathodes and lithium(Li)metal anodes,however,faces significant challenges under such demanding conditions.Herein,a nonflammable electrolyte for high-voltage Li‖LCO cells has been designed,including partially-fluorinated ethyl 4,4,4-trifluorobutyrate(ETFB)as the key solvent,guided by theoretical calculations.With this ETFB-based electrolyte,Li‖LCO cells exhibit enhanced reversible capacities and superior capacity retention at an elevated charge voltage of 4.5 V and a wide operating temperature range spanning from-60℃to 70℃.The cells achieve 67.1%discharge capacity at-60℃,relative to room temperature capacity,and 85.9%100th-cycle retention at 70℃.The outstanding properties are attributed to the LiF-rich interphases formed in the ETFB-based electrolyte with a finetuned solvation structure,in which the coordination environment in the vicinity of Li^(+)cations and the distance between anion and solvents are subtly adjusted by introducing ETFB.This solvation structure has been mutually elucidated through joint spectra characterizations and atomistic simulations.This work presents a new strategy for the design of electrolytes to achieve all-climate reliable and safe application of LMBs.展开更多
The method of time series analysis,applied by establishing appropriate mathematical models for bridge health monitoring data and making forecasts of structural future behavior,stands out as a novel and viable research...The method of time series analysis,applied by establishing appropriate mathematical models for bridge health monitoring data and making forecasts of structural future behavior,stands out as a novel and viable research direction for bridge state assessment.However,outliers inevitably exist in the monitoring data due to various interventions,which reduce the precision of model fitting and affect the forecasting results.Therefore,the identification of outliers is crucial for the accurate interpretation of the monitoring data.In this study,a time series model combined with outlier information for bridge health monitoring is established using intervention analysis theory,and the forecasting of the structural responses is carried out.There are three techniques that we focus on:(1)the modeling of seasonal autoregressive integrated moving average(SARIMA)model;(2)the methodology for outlier identification and amendment under the circumstances that the occurrence time and type of outliers are known and unknown;(3)forecasting of the model with outlier effects.The method was tested with a case study using monitoring data on a real bridge.The establishment of the original SARIMA model without considering outliers is first discussed,including the stationarity,order determination,parameter estimation and diagnostic checking of the model.Then the time-by-time iterative procedure for outlier detection,which is implemented by appropriate test statistics of the residuals,is performed.The SARIMA-outlier model is subsequently built.Finally,a comparative analysis of the forecasting performance between the original model and SARIMA-outlier model is carried out.The results demonstrate that proper time series models are effective in mining the characteristic law of bridge monitoring data.When the influence of outliers is taken into account,the fitted precision of the model is significantly improved and the accuracy and the reliability of the forecast are strengthened.展开更多
This article proposes a 35D form index system to quantitatively describe the 3D form of urban blocks.Utilizing the T-distributed stochastic neighbor(TSNE)embedding algorithm for cluster analysis,the visually complex a...This article proposes a 35D form index system to quantitatively describe the 3D form of urban blocks.Utilizing the T-distributed stochastic neighbor(TSNE)embedding algorithm for cluster analysis,the visually complex and disordered urban 3D texture is translated into distinct form clusters,enabling the recognition of the overall urban form structure from the block perspective.The research methodology includes experiments conducted in the central area of Nanjing and comparative analysis in three neighboring cities:Shanghai,Hangzhou,and Suzhou.Results demonstrate the efficacy of form parameters and cluster analysis in achieving sound recognition.The four cities differ remarkably in the number and distribution structure of clusters.Shanghai has the fewest types of clusters with a compact distribution,Suzhou has the most types with a dispersed distribution,and Hangzhou and Nanjing exhibit similar characteristics,located between Shanghai and Suzhou.Correlation analysis reveals a negative relationship between the number of cluster types and the level of urban socioeconomic development in similar areas.This research implies that governments and urban planners can exploit neighborhood morphological types to devise customized spatial management and renewal strategies.The overall urban structure can be improved by strategically minimizing the quantity and distribution of neighborhood morphological types,fostering socioeconomic development.展开更多
基金the financial support from Projects of Science&Technology Department of Sichuan Province(Grant No.22023YFG0082,Grant No.2023YFG0096,and Grant No.2023ZHJY0019)Chengdu Science and Technology Projects(Grant No.2024-YF08-00062-GX).
文摘Efficient,safe,and reliable energy output from high-energy-density lithium metal batteries(LMBs)at all climates is crucial for portable electronic devices operating in complex environments.The performance of corresponding cathodes and lithium(Li)metal anodes,however,faces significant challenges under such demanding conditions.Herein,a nonflammable electrolyte for high-voltage Li‖LCO cells has been designed,including partially-fluorinated ethyl 4,4,4-trifluorobutyrate(ETFB)as the key solvent,guided by theoretical calculations.With this ETFB-based electrolyte,Li‖LCO cells exhibit enhanced reversible capacities and superior capacity retention at an elevated charge voltage of 4.5 V and a wide operating temperature range spanning from-60℃to 70℃.The cells achieve 67.1%discharge capacity at-60℃,relative to room temperature capacity,and 85.9%100th-cycle retention at 70℃.The outstanding properties are attributed to the LiF-rich interphases formed in the ETFB-based electrolyte with a finetuned solvation structure,in which the coordination environment in the vicinity of Li^(+)cations and the distance between anion and solvents are subtly adjusted by introducing ETFB.This solvation structure has been mutually elucidated through joint spectra characterizations and atomistic simulations.This work presents a new strategy for the design of electrolytes to achieve all-climate reliable and safe application of LMBs.
基金funded by the Natural Science Foundation of Fujian Province(Grant No.2020J05207)Fujian University Engineering Research Center for Disaster Prevention and Mitigation of Engineering Structures along the Southeast Coast(Grant No.JDGC03)+1 种基金Major Scientific Research Platform Project of Putian City(Grant No.2021ZP03)Talent Introduction Project of Putian University(Grant No.2018074).
文摘The method of time series analysis,applied by establishing appropriate mathematical models for bridge health monitoring data and making forecasts of structural future behavior,stands out as a novel and viable research direction for bridge state assessment.However,outliers inevitably exist in the monitoring data due to various interventions,which reduce the precision of model fitting and affect the forecasting results.Therefore,the identification of outliers is crucial for the accurate interpretation of the monitoring data.In this study,a time series model combined with outlier information for bridge health monitoring is established using intervention analysis theory,and the forecasting of the structural responses is carried out.There are three techniques that we focus on:(1)the modeling of seasonal autoregressive integrated moving average(SARIMA)model;(2)the methodology for outlier identification and amendment under the circumstances that the occurrence time and type of outliers are known and unknown;(3)forecasting of the model with outlier effects.The method was tested with a case study using monitoring data on a real bridge.The establishment of the original SARIMA model without considering outliers is first discussed,including the stationarity,order determination,parameter estimation and diagnostic checking of the model.Then the time-by-time iterative procedure for outlier detection,which is implemented by appropriate test statistics of the residuals,is performed.The SARIMA-outlier model is subsequently built.Finally,a comparative analysis of the forecasting performance between the original model and SARIMA-outlier model is carried out.The results demonstrate that proper time series models are effective in mining the characteristic law of bridge monitoring data.When the influence of outliers is taken into account,the fitted precision of the model is significantly improved and the accuracy and the reliability of the forecast are strengthened.
基金We would also like to express our gratitude to the fellowship of China postdoctoral science foundation(Grant No.2021M700769)“Double Innovation”Doctor of Jiangsu Province(JSSCBS20220545)for funding this research project.
文摘This article proposes a 35D form index system to quantitatively describe the 3D form of urban blocks.Utilizing the T-distributed stochastic neighbor(TSNE)embedding algorithm for cluster analysis,the visually complex and disordered urban 3D texture is translated into distinct form clusters,enabling the recognition of the overall urban form structure from the block perspective.The research methodology includes experiments conducted in the central area of Nanjing and comparative analysis in three neighboring cities:Shanghai,Hangzhou,and Suzhou.Results demonstrate the efficacy of form parameters and cluster analysis in achieving sound recognition.The four cities differ remarkably in the number and distribution structure of clusters.Shanghai has the fewest types of clusters with a compact distribution,Suzhou has the most types with a dispersed distribution,and Hangzhou and Nanjing exhibit similar characteristics,located between Shanghai and Suzhou.Correlation analysis reveals a negative relationship between the number of cluster types and the level of urban socioeconomic development in similar areas.This research implies that governments and urban planners can exploit neighborhood morphological types to devise customized spatial management and renewal strategies.The overall urban structure can be improved by strategically minimizing the quantity and distribution of neighborhood morphological types,fostering socioeconomic development.
