Optimizing charge migration and alleviating volume expansion in anode materials are the key to improve the electrochemical performance for sodium-ion storage devices.Herein,a hierarchical porous conducting matrix conf...Optimizing charge migration and alleviating volume expansion in anode materials are the key to improve the electrochemical performance for sodium-ion storage devices.Herein,a hierarchical porous conducting matrix confining defect-rich selenium doped cobalt dichalcogenide(CoSe_(0.5)S_(1.5)/GA)is constructed as a promising SICs anode based on the guidance of theoretical calculation analysis.The increased defect concentration significantly enhanced the disorder degree of the compound and presented electron aggregation around the S atoms,which effectively modulated the electronic structure,further enabling high rate and ultra-capacity sodium storage.Moreover,strong interfacial coupling could construct spatial constraint to alleviate volume expansion as well as maintain electrode integrity and stability.The CoSe_(0.5)S_(1.5)/GA electrode can deliver a high capacity of 310.1 mA h g^(-1)after 2000 cycles at 1 A g^(-1),and the CoSe_(0.5)S_(1.5)/GA//AC sodium ion capacitor can exhibit an outstanding energy density of 237.5 W h kg^(-1).A series of characterization and theoretical calculation convincingly reveal that the defect moieties can regulate the Na^(+)storage and diffusion kinetics,which prove that our defect manufacture coupling with space-confined strategy can provide deep insights into the development of high-performance Na^(+)storage devices.展开更多
Zero defection manufacturing (ZDM) is the pursuit of the manufacturing industry. However, there is a lack of the implementation method of ZDM in the multi-stage manufacturing process (MMP). Implementing ZDM and contro...Zero defection manufacturing (ZDM) is the pursuit of the manufacturing industry. However, there is a lack of the implementation method of ZDM in the multi-stage manufacturing process (MMP). Implementing ZDM and controlling product quality in MMP remains an urgent problem in intelligent manufacturing. A novel predict-prevention quality control method in MMP towards ZDM is proposed, including quality characteristics monitoring, key quality characteristics prediction, and assembly quality optimization. The stability of the quality characteristics is detected by analyzing the distribution of quality characteristics. By considering the correlations between different quality characteristics, a deep supervised long-short term memory (SLSTM) prediction network is built for time series prediction of quality characteristics. A long-short term memory-genetic algorithm (LSTM-GA) network is proposed to optimize the assembly quality. By utilizing the proposed quality control method in MMP, unqualified products can be avoided, and ZDM of MMP is implemented. Extensive empirical evaluations on the MMP of compressors validate the applicability and practicability of the proposed method.展开更多
The quality of a product is dependent on both facilities/equipment and manufacturing processes. Any error or disorder in facilities and processes can cause a catastrophic failure. To avoid such failures, a zero- defec...The quality of a product is dependent on both facilities/equipment and manufacturing processes. Any error or disorder in facilities and processes can cause a catastrophic failure. To avoid such failures, a zero- defect manufacturing （ZDM） system is necessary in order to increase the reliability and safety of manufacturing systems and reach zero-defect quality of products. One of the major challenges for ZDM is the analysis of massive raw datasets. This type of analysis needs an automated and self-orga- nized decision making system. Data mining （DM） is an effective methodology for discovering interesting knowl- edge within a huge datasets. It plays an important role in developing a ZDM system. The paper presents a general framework of ZDM and explains how to apply DM approaches to manufacture the products with zero-defect. This paper also discusses 3 ongoing projects demonstrating the practice of using DM approaches for reaching the goal of ZDM.展开更多
基金financially supported by the National Nature Science Foundation of China(No.52202335)Natural Science Foundation of Jiangsu Province(No.BK20221137,BK20221139)。
文摘Optimizing charge migration and alleviating volume expansion in anode materials are the key to improve the electrochemical performance for sodium-ion storage devices.Herein,a hierarchical porous conducting matrix confining defect-rich selenium doped cobalt dichalcogenide(CoSe_(0.5)S_(1.5)/GA)is constructed as a promising SICs anode based on the guidance of theoretical calculation analysis.The increased defect concentration significantly enhanced the disorder degree of the compound and presented electron aggregation around the S atoms,which effectively modulated the electronic structure,further enabling high rate and ultra-capacity sodium storage.Moreover,strong interfacial coupling could construct spatial constraint to alleviate volume expansion as well as maintain electrode integrity and stability.The CoSe_(0.5)S_(1.5)/GA electrode can deliver a high capacity of 310.1 mA h g^(-1)after 2000 cycles at 1 A g^(-1),and the CoSe_(0.5)S_(1.5)/GA//AC sodium ion capacitor can exhibit an outstanding energy density of 237.5 W h kg^(-1).A series of characterization and theoretical calculation convincingly reveal that the defect moieties can regulate the Na^(+)storage and diffusion kinetics,which prove that our defect manufacture coupling with space-confined strategy can provide deep insights into the development of high-performance Na^(+)storage devices.
基金The research work presented in this paper is supported by the National Natural Science Foundation of China(Grant No.51675418).
文摘Zero defection manufacturing (ZDM) is the pursuit of the manufacturing industry. However, there is a lack of the implementation method of ZDM in the multi-stage manufacturing process (MMP). Implementing ZDM and controlling product quality in MMP remains an urgent problem in intelligent manufacturing. A novel predict-prevention quality control method in MMP towards ZDM is proposed, including quality characteristics monitoring, key quality characteristics prediction, and assembly quality optimization. The stability of the quality characteristics is detected by analyzing the distribution of quality characteristics. By considering the correlations between different quality characteristics, a deep supervised long-short term memory (SLSTM) prediction network is built for time series prediction of quality characteristics. A long-short term memory-genetic algorithm (LSTM-GA) network is proposed to optimize the assembly quality. By utilizing the proposed quality control method in MMP, unqualified products can be avoided, and ZDM of MMP is implemented. Extensive empirical evaluations on the MMP of compressors validate the applicability and practicability of the proposed method.
文摘The quality of a product is dependent on both facilities/equipment and manufacturing processes. Any error or disorder in facilities and processes can cause a catastrophic failure. To avoid such failures, a zero- defect manufacturing （ZDM） system is necessary in order to increase the reliability and safety of manufacturing systems and reach zero-defect quality of products. One of the major challenges for ZDM is the analysis of massive raw datasets. This type of analysis needs an automated and self-orga- nized decision making system. Data mining （DM） is an effective methodology for discovering interesting knowl- edge within a huge datasets. It plays an important role in developing a ZDM system. The paper presents a general framework of ZDM and explains how to apply DM approaches to manufacture the products with zero-defect. This paper also discusses 3 ongoing projects demonstrating the practice of using DM approaches for reaching the goal of ZDM.
