WITHDRAWAL:Zhang,J.J.,Guo,Y.Q.,Qin,Z.Y.,Wei,C.T.,Hu,Q.H.,Vandeginste,V.,Miao,H.Y.,Yao,P.,and Zhang,P.F.,“Predicting Irreducible Water Saturation of Unconventional Reservoirs by Using NMR T2 Spectra:Methods of Morphol...WITHDRAWAL:Zhang,J.J.,Guo,Y.Q.,Qin,Z.Y.,Wei,C.T.,Hu,Q.H.,Vandeginste,V.,Miao,H.Y.,Yao,P.,and Zhang,P.F.,“Predicting Irreducible Water Saturation of Unconventional Reservoirs by Using NMR T2 Spectra:Methods of Morphological Division and Fractal Models”,Acta Geologica Sinica-English Edition(Accepted Article):https://doi.org/10.1111/1755-6724.15094.展开更多
To ensure the safe operation of batteries,accurately obtaining key internal state parameters is essential.However,traditional parameter measurement methods either require opening the battery or long-term measurements,...To ensure the safe operation of batteries,accurately obtaining key internal state parameters is essential.However,traditional parameter measurement methods either require opening the battery or long-term measurements,which are impractical.Therefore,the fixed values are commonly used for these parameters in electrochemical models and have significant limitations.To overcome these limitations,this paper proposes a deep neural network(DNN)based data-driven evaluation method to determine model parameters.By coupling an improved one-dimensional isothermal pseudo-twodimensional(P2D)model with DNN,this study identified concentration-dependent parameters through detailed discharge curve analysis.The results show that the data-driven method can effectively obtain the change trend of concentration-dependent parameters through the charge and discharge curve,and the method can be extended to different battery systems in different discharge rates and aging applications.This work is expected to provide new parameter selection insights for data-driven battery prediction and monitoring models.展开更多
Complex phase transitions occur in P2-type materials during charging and discharging.A high-entropy structure can effectively inhibit the structural phase transition of a P2-type layered material.In this study,a hight...Complex phase transitions occur in P2-type materials during charging and discharging.A high-entropy structure can effectively inhibit the structural phase transition of a P2-type layered material.In this study,a hightemperature solid-phase method is used to synthesize the P2-type high-entropy fluorine oxide(HEFO)Na_(0.7)Li_(0.08)Mn(Ⅳ)_(0.21)Mn(Ⅲ)_(0.43)Mg_(0.11)Ni_(0.11)W_(0.04)Nb_(0.02)O_(1.9)F_(0.1)[■-NLM(Ⅳ)0.21M(Ⅲ)0.43F(■=NMNWO)],with a superlattice structure and Na_(2)WO_(4)coating.Na_(2)WO_(4)can effectively inhibit the complex phase transition to improve the structural stability of the material and overcome the limitations of P2-type Na_(x)TMO_(2)(TM=transition metal)via additional charge compensation.Adjusting the Mn^(3+)/Mn^(4+)ratio to increase the average valence state of Mn and introducing F^(-)and Li^(+)to inhibit the Jahn-Teller effect suppress the complex phase transition during charging and discharging.The material exhibits a good multiplicative performance(discharge specific capacity of 88.4 mAh g^(-1)at a multiplicative rate of 10C)and capacity retention(99.22%after 200 cycles at 1C in the potential window of 1.5-4.3 V).The structural stabilities of HEFO are effectively demonstrated using electrochemical in situ X-ray diffraction and ex situ X-ray photoelectron spectroscopy.Theoretical calculations reveal that the high-entropy structure effectively improves the electronic structure and charge distribution of the layered oxide material.This study provides new concepts for use in developing novel energy batteries.展开更多
文摘WITHDRAWAL:Zhang,J.J.,Guo,Y.Q.,Qin,Z.Y.,Wei,C.T.,Hu,Q.H.,Vandeginste,V.,Miao,H.Y.,Yao,P.,and Zhang,P.F.,“Predicting Irreducible Water Saturation of Unconventional Reservoirs by Using NMR T2 Spectra:Methods of Morphological Division and Fractal Models”,Acta Geologica Sinica-English Edition(Accepted Article):https://doi.org/10.1111/1755-6724.15094.
基金supported by National Natural Science Foundation of China(22478239)Science and Technology Commission of Shanghai Municipality(19DZ2271100)National Natural Science Foundation of China(22208208)。
文摘To ensure the safe operation of batteries,accurately obtaining key internal state parameters is essential.However,traditional parameter measurement methods either require opening the battery or long-term measurements,which are impractical.Therefore,the fixed values are commonly used for these parameters in electrochemical models and have significant limitations.To overcome these limitations,this paper proposes a deep neural network(DNN)based data-driven evaluation method to determine model parameters.By coupling an improved one-dimensional isothermal pseudo-twodimensional(P2D)model with DNN,this study identified concentration-dependent parameters through detailed discharge curve analysis.The results show that the data-driven method can effectively obtain the change trend of concentration-dependent parameters through the charge and discharge curve,and the method can be extended to different battery systems in different discharge rates and aging applications.This work is expected to provide new parameter selection insights for data-driven battery prediction and monitoring models.
基金financially supported by the Guizhou Provincial Basic Research Program(Natural Science)(No.QKHJC-ZK[2023]YB051)the Natural Science Special Foundation of Guizhou University(No.GDTGH[2022]33)+2 种基金the Natural Science Research project of the Education Department of Guizhou Province(No.QJJ[2022]001)the National Natural Science Foundation of China(No.52161029)the Science and Technology Innovation Team of Education Agency in Guizhou Province(No.Qian Jiao Ji[2023]056)
文摘Complex phase transitions occur in P2-type materials during charging and discharging.A high-entropy structure can effectively inhibit the structural phase transition of a P2-type layered material.In this study,a hightemperature solid-phase method is used to synthesize the P2-type high-entropy fluorine oxide(HEFO)Na_(0.7)Li_(0.08)Mn(Ⅳ)_(0.21)Mn(Ⅲ)_(0.43)Mg_(0.11)Ni_(0.11)W_(0.04)Nb_(0.02)O_(1.9)F_(0.1)[■-NLM(Ⅳ)0.21M(Ⅲ)0.43F(■=NMNWO)],with a superlattice structure and Na_(2)WO_(4)coating.Na_(2)WO_(4)can effectively inhibit the complex phase transition to improve the structural stability of the material and overcome the limitations of P2-type Na_(x)TMO_(2)(TM=transition metal)via additional charge compensation.Adjusting the Mn^(3+)/Mn^(4+)ratio to increase the average valence state of Mn and introducing F^(-)and Li^(+)to inhibit the Jahn-Teller effect suppress the complex phase transition during charging and discharging.The material exhibits a good multiplicative performance(discharge specific capacity of 88.4 mAh g^(-1)at a multiplicative rate of 10C)and capacity retention(99.22%after 200 cycles at 1C in the potential window of 1.5-4.3 V).The structural stabilities of HEFO are effectively demonstrated using electrochemical in situ X-ray diffraction and ex situ X-ray photoelectron spectroscopy.Theoretical calculations reveal that the high-entropy structure effectively improves the electronic structure and charge distribution of the layered oxide material.This study provides new concepts for use in developing novel energy batteries.
