Porous spherical MnCo_(2)S_(4) was synthesized by a simple solvothermal method.Thanks to the well-designedbimetallic composition and the unique porous spherical structure,the MnCo_(2)S_(4) electrode exhibited an excep...Porous spherical MnCo_(2)S_(4) was synthesized by a simple solvothermal method.Thanks to the well-designedbimetallic composition and the unique porous spherical structure,the MnCo_(2)S_(4) electrode exhibited an exceptionalspecific capacitance of 190.8 mAh·g^(-1)at 1 A·g^(-1),greatly higher than the corresponding monometallic sulfides MnS(31.7 mAh·g^(-1))and Co_(3)S_(4)(86.7 mAh·g^(-1)).Impressively,the as-assembled MnCo_(2)S_(4)||porous carbon(PC)hybridsupercapacitor(HSC),showed an outstanding energy density of 76.88 Wh·kg^(-1)at a power density of 374.5 W·kg^(-1),remarkable cyclic performance with a capacity retention of 86.8% after 10000 charge-discharge cycles at 5 A·g^(-1),and excellent Coulombic efficiency of 99.7%.展开更多
The triple bond in N_(2)has an extremely high bond energy and is thus difficult to break.N_(2)is commonly converted into NH3 artificially via the Haber-Bosch process,and NH_(3)can be utilized to produce other nitrogen...The triple bond in N_(2)has an extremely high bond energy and is thus difficult to break.N_(2)is commonly converted into NH3 artificially via the Haber-Bosch process,and NH_(3)can be utilized to produce other nitrogen-containing chemicals.Here,we developed an electron catalyzed method to directly fix N_(2)into azos,by pushing and pulling the electron into and from the aromatic halide with the cyclic voltammetry method.The round-trip journey of electron can successfully weaken the triple bond in N_(2)through the electron pushing-induced aryl radical via a“brick trowel”transition state,and then produce the diazonium ions by pulling the electron out from the diazo radical intermediate.Different azos can be synthesized with this developed electron catalyzed approach.This approach provides a novel concept and practical route for the fixation of N_(2)at atmospheric pressure into chemical products valuable for industrial and commercial applications.展开更多
The degradation process of lithium-ion batteries is intricately linked to their entire lifecycle as power sources and energy storage devices,encompassing aspects such as performance delivery and cycling utilization.Co...The degradation process of lithium-ion batteries is intricately linked to their entire lifecycle as power sources and energy storage devices,encompassing aspects such as performance delivery and cycling utilization.Consequently,the accurate and expedient estimation or prediction of the aging state of lithium-ion batteries has garnered extensive attention.Nonetheless,prevailing research predominantly concentrates on either aging estimation or prediction,neglecting the dynamic fusion of both facets.This paper proposes a hybrid model for capacity aging estimation and prediction based on deep learning,wherein salient features highly pertinent to aging are extracted from charge and discharge relaxation processes.By amalgamating historical capacity decay data,the model dynamically furnishes estimations of the present capacity and forecasts of future capacity for lithium-ion batteries.Our approach is validated against a novel dataset involving charge and discharge cycles at varying rates.Specifically,under a charging condition of 0.25 C,a mean absolute percentage error(MAPE)of 0.29%is achieved.This outcome underscores the model's adeptness in harnessing relaxation processes commonly encountered in the real world and synergizing with historical capacity records within battery management systems(BMS),thereby affording estimations and prognostications of capacity decline with heightened precision.展开更多
文摘Porous spherical MnCo_(2)S_(4) was synthesized by a simple solvothermal method.Thanks to the well-designedbimetallic composition and the unique porous spherical structure,the MnCo_(2)S_(4) electrode exhibited an exceptionalspecific capacitance of 190.8 mAh·g^(-1)at 1 A·g^(-1),greatly higher than the corresponding monometallic sulfides MnS(31.7 mAh·g^(-1))and Co_(3)S_(4)(86.7 mAh·g^(-1)).Impressively,the as-assembled MnCo_(2)S_(4)||porous carbon(PC)hybridsupercapacitor(HSC),showed an outstanding energy density of 76.88 Wh·kg^(-1)at a power density of 374.5 W·kg^(-1),remarkable cyclic performance with a capacity retention of 86.8% after 10000 charge-discharge cycles at 5 A·g^(-1),and excellent Coulombic efficiency of 99.7%.
文摘The triple bond in N_(2)has an extremely high bond energy and is thus difficult to break.N_(2)is commonly converted into NH3 artificially via the Haber-Bosch process,and NH_(3)can be utilized to produce other nitrogen-containing chemicals.Here,we developed an electron catalyzed method to directly fix N_(2)into azos,by pushing and pulling the electron into and from the aromatic halide with the cyclic voltammetry method.The round-trip journey of electron can successfully weaken the triple bond in N_(2)through the electron pushing-induced aryl radical via a“brick trowel”transition state,and then produce the diazonium ions by pulling the electron out from the diazo radical intermediate.Different azos can be synthesized with this developed electron catalyzed approach.This approach provides a novel concept and practical route for the fixation of N_(2)at atmospheric pressure into chemical products valuable for industrial and commercial applications.
文摘The degradation process of lithium-ion batteries is intricately linked to their entire lifecycle as power sources and energy storage devices,encompassing aspects such as performance delivery and cycling utilization.Consequently,the accurate and expedient estimation or prediction of the aging state of lithium-ion batteries has garnered extensive attention.Nonetheless,prevailing research predominantly concentrates on either aging estimation or prediction,neglecting the dynamic fusion of both facets.This paper proposes a hybrid model for capacity aging estimation and prediction based on deep learning,wherein salient features highly pertinent to aging are extracted from charge and discharge relaxation processes.By amalgamating historical capacity decay data,the model dynamically furnishes estimations of the present capacity and forecasts of future capacity for lithium-ion batteries.Our approach is validated against a novel dataset involving charge and discharge cycles at varying rates.Specifically,under a charging condition of 0.25 C,a mean absolute percentage error(MAPE)of 0.29%is achieved.This outcome underscores the model's adeptness in harnessing relaxation processes commonly encountered in the real world and synergizing with historical capacity records within battery management systems(BMS),thereby affording estimations and prognostications of capacity decline with heightened precision.
