The recycling of spent batteries has become increasingly important owing to their wide applications,abundant raw material supply,and sustainable development.Compared with the degraded cathode,spent anode graphite ofte...The recycling of spent batteries has become increasingly important owing to their wide applications,abundant raw material supply,and sustainable development.Compared with the degraded cathode,spent anode graphite often has a relatively intact structure with few defects after long cycling.Yet,most spent graphite is simply burned or discarded due to its limited value and inferior performance on using conventional recycling methods that are complex,have low efficiency,and fail in performance restoration.Herein,we propose a fast,efficient,and“intelligent”strategy to regenerate and upcycle spent graphite based on defect‐driven targeted remediation.Using Sn as a nanoscale healant,we used rapid heating(~50 ms)to enable dynamic Sn droplets to automatically nucleate around the surface defects on the graphite upon cooling owing to strong binding to the defects(~5.84 eV/atom),thus simultaneously achieving Sn dispersion and graphite remediation.As a result,the regenerated graphite showed enhanced capacity and cycle stability(458.9 mAh g^(−1) at 0.2 A g^(−1) after 100 cycles),superior to those of commercial graphite.Benefiting from the self‐adaption of Sn dispersion,spent graphite with different degrees of defects can be regenerated to similar structures and performance.EverBatt analysis indicates that targeted regeneration and upcycling have significantly lower energy consumption(~99%reduction)and near‐zero CO2 emission,and yield much higher profit than hydrometallurgy,which opens a new avenue for direct upcycling of spend graphite in an efficient,green,and profitable manner for sustainable battery manufacture.展开更多
Coal and coalbed methane(CBM)coordinated exploitation is a key technology for the safe exploitation of both resources.However,existing studies lack the quantification and evaluation of the degree of coordination betwe...Coal and coalbed methane(CBM)coordinated exploitation is a key technology for the safe exploitation of both resources.However,existing studies lack the quantification and evaluation of the degree of coordination between coal mining and coalbed methane extraction.In this study,the concept of coal and coalbed methane coupling coordinated exploitation was proposed,and the corresponding evaluation model was established using the Bayesian principle.On this basis,the objective function of coal and coalbed methane coordinated exploitation deployment was established,and the optimal deployment was determined through a cuckoo search.The results show that clarifying the coupling coordinated level of coal and coalbed methane resource exploitation in coal mines is conducive to adjusting the deployment plan in advance.The case study results show that the evaluation and intelligent deployment method proposed in this paper can effectively evaluate the coupling coordinated level of coal and coalbed methane resource exploitation and intelligently optimize the deployment of coal mine operations.The optimization results demonstrate that the safe and efficient exploitation of coal and CBM resources is promoted,and coal mining and coalbed methane extraction processes show greater cooperation.The observations and findings of this study provide a critical reference for coal mine resource exploitation in the future.展开更多
Advanced electronic materials are the fundamental building blocks of integrated circuits(ICs).The microscale properties of electronic materials(e.g.,crystal structures,defects,and chemical properties)can have a consid...Advanced electronic materials are the fundamental building blocks of integrated circuits(ICs).The microscale properties of electronic materials(e.g.,crystal structures,defects,and chemical properties)can have a considerable impact on the performance of ICs.Comprehensive characterization and analysis of the material in real time with high-spatial resolution are indispensable.In situ transmission electron microscope(TEM)with atomic resolution and external field can be applied as a physical simulation platform to study the evolution of electronic material in working conditions.The high-speed camera of the in situ TEM generates a high frame rate video,resulting in a large dataset that is beyond the data processing ability of researchers using the traditional method.To overcome this challenge,many works on automated TEM analysis by using machine-learning algorithm have been proposed.In this review,we introduce the technical evolution of TEM data acquisition,including analysis,and we summarize the application of machine learning to TEM data analysis in the aspects of morphology,defect,structure,and spectra.Some of the challenges of automated TEM analysis are given in the conclusion.展开更多
In this paper,a general scheme in digital self-interference cancellation at baseband for zero-IF full-duplex transceivers is presented. We model the self-interference signals specifically with only the nonlinear disto...In this paper,a general scheme in digital self-interference cancellation at baseband for zero-IF full-duplex transceivers is presented. We model the self-interference signals specifically with only the nonlinear distortion signals falling in receiving band considered. A joint estimation algorithm is proposed for compensating the time delay and frequency offset taking into account the IQ amplitude and phase imbalances from mixers. The memory effect and nonlinear distortion are adaptively estimated by the de-correlated normalized least mean square(DNLMS) algorithm. Numerical simulation results demonstrate that the proposed self-interference cancellation scheme can efficiently compensate the self-interference and outperform the existing traditional solutions.展开更多
基金The Fundamental Research Funds for the Central Universities,HUST,Grant/Award Number:2021GCRC046The Open Fund of State Key Laboratory of New Textile Materials and Advanced Processing Technologies,Grant/Award Number:FZ2022005Natural Science Foundation of Hubei Province,China,Grant/Award Number:2022CFA031。
文摘The recycling of spent batteries has become increasingly important owing to their wide applications,abundant raw material supply,and sustainable development.Compared with the degraded cathode,spent anode graphite often has a relatively intact structure with few defects after long cycling.Yet,most spent graphite is simply burned or discarded due to its limited value and inferior performance on using conventional recycling methods that are complex,have low efficiency,and fail in performance restoration.Herein,we propose a fast,efficient,and“intelligent”strategy to regenerate and upcycle spent graphite based on defect‐driven targeted remediation.Using Sn as a nanoscale healant,we used rapid heating(~50 ms)to enable dynamic Sn droplets to automatically nucleate around the surface defects on the graphite upon cooling owing to strong binding to the defects(~5.84 eV/atom),thus simultaneously achieving Sn dispersion and graphite remediation.As a result,the regenerated graphite showed enhanced capacity and cycle stability(458.9 mAh g^(−1) at 0.2 A g^(−1) after 100 cycles),superior to those of commercial graphite.Benefiting from the self‐adaption of Sn dispersion,spent graphite with different degrees of defects can be regenerated to similar structures and performance.EverBatt analysis indicates that targeted regeneration and upcycling have significantly lower energy consumption(~99%reduction)and near‐zero CO2 emission,and yield much higher profit than hydrometallurgy,which opens a new avenue for direct upcycling of spend graphite in an efficient,green,and profitable manner for sustainable battery manufacture.
基金supported by the Natural Science Foundation of Chongqing,China(No.cstc2020jcyj-msxmX0836)the Fundamental Research Funds for the Central Universities(No.2020CDJ-LHZZ-002)the National Natural Science Foundation of China(No.52074041).
文摘Coal and coalbed methane(CBM)coordinated exploitation is a key technology for the safe exploitation of both resources.However,existing studies lack the quantification and evaluation of the degree of coordination between coal mining and coalbed methane extraction.In this study,the concept of coal and coalbed methane coupling coordinated exploitation was proposed,and the corresponding evaluation model was established using the Bayesian principle.On this basis,the objective function of coal and coalbed methane coordinated exploitation deployment was established,and the optimal deployment was determined through a cuckoo search.The results show that clarifying the coupling coordinated level of coal and coalbed methane resource exploitation in coal mines is conducive to adjusting the deployment plan in advance.The case study results show that the evaluation and intelligent deployment method proposed in this paper can effectively evaluate the coupling coordinated level of coal and coalbed methane resource exploitation and intelligently optimize the deployment of coal mine operations.The optimization results demonstrate that the safe and efficient exploitation of coal and CBM resources is promoted,and coal mining and coalbed methane extraction processes show greater cooperation.The observations and findings of this study provide a critical reference for coal mine resource exploitation in the future.
基金supported by NSFC under Grant Nos.62074057,62174056Projects of Science and Technology Commission of Shanghai Municipality Grant Nos.(19ZR1473800,18DZ2270800)+1 种基金‘Shuguang Program’supported by Shanghai Education Development Foundation and Shanghai Municipal Education Commissionthe Fundamental Research Funds for the Central Universities.
文摘Advanced electronic materials are the fundamental building blocks of integrated circuits(ICs).The microscale properties of electronic materials(e.g.,crystal structures,defects,and chemical properties)can have a considerable impact on the performance of ICs.Comprehensive characterization and analysis of the material in real time with high-spatial resolution are indispensable.In situ transmission electron microscope(TEM)with atomic resolution and external field can be applied as a physical simulation platform to study the evolution of electronic material in working conditions.The high-speed camera of the in situ TEM generates a high frame rate video,resulting in a large dataset that is beyond the data processing ability of researchers using the traditional method.To overcome this challenge,many works on automated TEM analysis by using machine-learning algorithm have been proposed.In this review,we introduce the technical evolution of TEM data acquisition,including analysis,and we summarize the application of machine learning to TEM data analysis in the aspects of morphology,defect,structure,and spectra.Some of the challenges of automated TEM analysis are given in the conclusion.
基金supported in part by the National Natural Science Foundation of China(No.61601027)
文摘In this paper,a general scheme in digital self-interference cancellation at baseband for zero-IF full-duplex transceivers is presented. We model the self-interference signals specifically with only the nonlinear distortion signals falling in receiving band considered. A joint estimation algorithm is proposed for compensating the time delay and frequency offset taking into account the IQ amplitude and phase imbalances from mixers. The memory effect and nonlinear distortion are adaptively estimated by the de-correlated normalized least mean square(DNLMS) algorithm. Numerical simulation results demonstrate that the proposed self-interference cancellation scheme can efficiently compensate the self-interference and outperform the existing traditional solutions.