The bare LiFePO4 and LiFePO4/C composites with network structure were prepared by solid-state reaction. The crystalline structures, morphologies and specific surface areas of the materials were investigated by X-ray d...The bare LiFePO4 and LiFePO4/C composites with network structure were prepared by solid-state reaction. The crystalline structures, morphologies and specific surface areas of the materials were investigated by X-ray diffractometry(XRD), scanning electron microscopy(SEM) and multi-point brunauer emmett and teller(BET) method. The results show that the LiFePO4/C composite with the best network structure is obtained by adding 10% phenolic resin carbon. Its electronic conductivity increases to 2.86×10-2 S/cm. It possesses the highest specific surface area of 115.65 m2/g, which exhibits the highest discharge specific capacity of 164.33 mA·h/g at C/10 rate and 149.12 mA·h/g at 1 C rate. The discharge capacity is completely recovered when C/10 rate is applied again.展开更多
Ion temperature, as one of the most critical plasma parameters, can be diagnosed by charge exchange recombination spectroscopy (CXRS). Iterative least-squares fitting is conventionally used to analyze CXRS spectra to ...Ion temperature, as one of the most critical plasma parameters, can be diagnosed by charge exchange recombination spectroscopy (CXRS). Iterative least-squares fitting is conventionally used to analyze CXRS spectra to identify the active charge exchange component, which is the result of local interaction between impurity ions with a neutral beam. Due to the limit of the time consumption of the conventional approach (~100 ms per frame), the Experimental Advanced Superconducting Tokamak CXRS data is now analyzed in-between shots. To explore the feasibility of real-time measurement, neural networks are introduced to perform fast estimation of ion temperature. Based on the same four-layer neural network architecture, two neural networks are trained for two central chords according to the ion temperature data acquired from the conventional method. Using the TensorFlow framework, the training procedures are performed by an error back-propagation algorithm with the regularization via the weight decay method. Good agreement in the deduced ion temperature is shown for the neural networks and the conventional approach, while the data processing time is reduced by 3 orders of magnitude (~0.1 ms per frame) by using the neural networks.展开更多
In this paper,we investigate the evolution of spatiotemporal patterns and synchronization transitions in dependence on the information transmission delay and ion channel blocking in scale-free neuronal networks.As the...In this paper,we investigate the evolution of spatiotemporal patterns and synchronization transitions in dependence on the information transmission delay and ion channel blocking in scale-free neuronal networks.As the underlying model of neuronal dynamics,we use the Hodgkin-Huxley equations incorporating channel blocking and intrinsic noise.It is shown that delays play a significant yet subtle role in shaping the dynamics of neuronal networks.In particular,regions of irregular and regular propagating excitatory fronts related to the synchronization transitions appear intermittently as the delay increases.Moreover,the fraction of working sodium and potassium ion channels can also have a significant impact on the spatiotemporal dynamics of neuronal networks.As the fraction of blocked sodium channels increases,the frequency of excitatory events decreases,which in turn manifests as an increase in the neuronal synchrony that,however,is dysfunctional due to the virtual absence of large-amplitude excitations.Expectedly,we also show that larger coupling strengths improve synchronization irrespective of the information transmission delay and channel blocking.The presented results are also robust against the variation of the network size,thus providing insights that could facilitate understanding of the joint impact of ion channel blocking and information transmission delay on the spatiotemporal dynamics of neuronal networks.展开更多
A random two-dimensional large scale nano-network of silver nanowires （Ag-NWs） is fabricated by MeV hydrogen （H＋） ion beam irradiation. Ag-NWs are irradiated under H＋ ion beam at different ion fluences at room t...A random two-dimensional large scale nano-network of silver nanowires （Ag-NWs） is fabricated by MeV hydrogen （H＋） ion beam irradiation. Ag-NWs are irradiated under H＋ ion beam at different ion fluences at room temperature. The Ag-NW network is fabricated by H＋ ion beam-induced welding of Ag-NWs at intersecting positions. H＋ ion beam induced welding is confirmed by transmission electron microscopy （TEM） and scanning electron microscopy （SEM）. Moreover, the structure of Ag NWs remains stable under H＋ ion beam, and networks are optically transparent. Morphology also remains stable under H＋ ion beam irradiation. No slicings or cuttings of Ag-NWs are observed under MeV H＋ ion beam irradiation. The results exhibit that the formation of Ag-NW network proceeds through three steps： ion beam induced thermal spikes lead to the local heating of Ag-NWs, the formation of simple junctions on small scale, and the formation of a large scale network. This observation is useful for using Ag-NWs based devices in upper space where protons are abandoned in an energy range from MeV to GeV. This high-quality Ag-NW network can also be used as a transparent electrode for optoelectronics devices.展开更多
为了准确判断电池可用容量,采用长短期记忆神经网络对电池容量进行估算。首先分析电池各参数全生命周期变化曲线,计算其与电池容量之间的皮尔逊相关系数,选择电池电压、内阻、等压降时间等参数作为健康因子构建电池容量估计模型。使用...为了准确判断电池可用容量,采用长短期记忆神经网络对电池容量进行估算。首先分析电池各参数全生命周期变化曲线,计算其与电池容量之间的皮尔逊相关系数,选择电池电压、内阻、等压降时间等参数作为健康因子构建电池容量估计模型。使用美国先进寿命周期工程中心CALCE(Center for Advanced Life Cycle Engineering)电池数据集进行模型训练并估算电池容量,估计模型的平均百分误差为1.19%。分析估算误差产生的原因,通过电池初始容量参数修正和电池老化参数修正进行模型优化。优化结果表明,使用电池电压、内阻、恒流充电时间和4.0~3.4 V等压降时间构建模型估计误差在0.55%左右。展开更多
锂离子电池荷电状态(state of charge,SOC)的精确估计对储能系统及电动汽车能源管理至关重要。为解决现有单一神经网络架构在复杂工况下的SOC估计精度不足问题,提出一种基于卷积历史序列分解混合(convolutional past decomposable mixin...锂离子电池荷电状态(state of charge,SOC)的精确估计对储能系统及电动汽车能源管理至关重要。为解决现有单一神经网络架构在复杂工况下的SOC估计精度不足问题,提出一种基于卷积历史序列分解混合(convolutional past decomposable mixing,CPDM)-长短期记忆(long short-term memory,LSTM)网络的混合估计模型。首先,通过平均池化方法与一维卷积神经网络对电池数据构建并提取多尺度时序特征;其次,利用CPDM模块对序列进行跨尺度分解与混合,以增强信息互补;最后,将增强的多尺度序列并行输入LSTM网络进行预测,并通过等权相加各尺度预测值得到SOC估计结果。实验结果表明,CPDM-LSTM模型在公开数据集上的SOC估计性能良好。其在不同温度及工况下的平均均方根误差为0.0485,平均绝对误差为0.0371,验证了模型较强的鲁棒性和泛化能力。展开更多
By incorporating copper sulfate （CuSO4） particles into acrylonitrile butadiene rubber （NBR） followed by heat pressing, a novel vulcanization method is developed in rubber through the formation of coordination cros...By incorporating copper sulfate （CuSO4） particles into acrylonitrile butadiene rubber （NBR） followed by heat pressing, a novel vulcanization method is developed in rubber through the formation of coordination crosslinking. This method totally differs from traditional covalent or non-covalent vulcanization approaches of rubber. No other vulcanizing agent or additional additive is involved in this process. By analyzing the results of DMA, XPS and FT-IR, it is found that the crosslinking of CuSO4 particles filled NBR was induced by in situ coordination between nitrogen atoms of nitrile groups （-CN） and copper ions （Cu^2＋） from CuSO4. SEM and EDX results revealed the generation of a core （CuSO4 solid particle）- shell （adherent NBR） structure, which leads to a result that the crosslinked rubber has excellent mechanical properties. Moreover, poly（vinyl chloride） （PVC） and liquid acrylonitrile-butadiene rubber （LNBR） were used as mobilizer to improve the coordination crosslinking of CuSO4/NBR. The addition of PVC or LNBR could lead to higher crosslink density and better mechanical properties of coordination vulcanization. In addition, crystal water in CuSO4 played a positive role to coordination crosslinking of rubber because it decreased the metal point of CuSO4 and promoted the metal ionization.展开更多
基金Project(50672024) supported by the National Natural Science Foundation of ChinaProject(06FJ2006) supported by the Applied Basic Research of Hunan Province, China
文摘The bare LiFePO4 and LiFePO4/C composites with network structure were prepared by solid-state reaction. The crystalline structures, morphologies and specific surface areas of the materials were investigated by X-ray diffractometry(XRD), scanning electron microscopy(SEM) and multi-point brunauer emmett and teller(BET) method. The results show that the LiFePO4/C composite with the best network structure is obtained by adding 10% phenolic resin carbon. Its electronic conductivity increases to 2.86×10-2 S/cm. It possesses the highest specific surface area of 115.65 m2/g, which exhibits the highest discharge specific capacity of 164.33 mA·h/g at C/10 rate and 149.12 mA·h/g at 1 C rate. The discharge capacity is completely recovered when C/10 rate is applied again.
基金supported by National Natural Science Foundation of China(No.11535013)the National Key Research and Development Program of China(Nos.2017YFA0402500,2018YFE0302100)the Users with Excellence Project of Hefei Science Center CAS(No.2018HSC-UE010)
文摘Ion temperature, as one of the most critical plasma parameters, can be diagnosed by charge exchange recombination spectroscopy (CXRS). Iterative least-squares fitting is conventionally used to analyze CXRS spectra to identify the active charge exchange component, which is the result of local interaction between impurity ions with a neutral beam. Due to the limit of the time consumption of the conventional approach (~100 ms per frame), the Experimental Advanced Superconducting Tokamak CXRS data is now analyzed in-between shots. To explore the feasibility of real-time measurement, neural networks are introduced to perform fast estimation of ion temperature. Based on the same four-layer neural network architecture, two neural networks are trained for two central chords according to the ion temperature data acquired from the conventional method. Using the TensorFlow framework, the training procedures are performed by an error back-propagation algorithm with the regularization via the weight decay method. Good agreement in the deduced ion temperature is shown for the neural networks and the conventional approach, while the data processing time is reduced by 3 orders of magnitude (~0.1 ms per frame) by using the neural networks.
基金supported by the National Natural Science Foundation of China(11172017 and 10972001)the Fujian Natural Science Foundation of China(2009J05004)a Key Project of Fujian Provincial Universities(Information Technology Research Based on Mathematics)
文摘In this paper,we investigate the evolution of spatiotemporal patterns and synchronization transitions in dependence on the information transmission delay and ion channel blocking in scale-free neuronal networks.As the underlying model of neuronal dynamics,we use the Hodgkin-Huxley equations incorporating channel blocking and intrinsic noise.It is shown that delays play a significant yet subtle role in shaping the dynamics of neuronal networks.In particular,regions of irregular and regular propagating excitatory fronts related to the synchronization transitions appear intermittently as the delay increases.Moreover,the fraction of working sodium and potassium ion channels can also have a significant impact on the spatiotemporal dynamics of neuronal networks.As the fraction of blocked sodium channels increases,the frequency of excitatory events decreases,which in turn manifests as an increase in the neuronal synchrony that,however,is dysfunctional due to the virtual absence of large-amplitude excitations.Expectedly,we also show that larger coupling strengths improve synchronization irrespective of the information transmission delay and channel blocking.The presented results are also robust against the variation of the network size,thus providing insights that could facilitate understanding of the joint impact of ion channel blocking and information transmission delay on the spatiotemporal dynamics of neuronal networks.
基金supported by the National Research Foundation of South Africa(NRF),the French Centre National pour la Recherche Scientifique,iThemba-LABS,the UNESCO-UNISA Africa Chair in Nanosciences & Nanotechnology,the Third World Academy of Science(TWAS),Organization of Women in Science for the Developing World(OWSDW),the Abdus Salam ICTP via the Nanosciences African Network(NANOAFNET),and the Higher Education Commission(HEC)of Pakistan
文摘A random two-dimensional large scale nano-network of silver nanowires （Ag-NWs） is fabricated by MeV hydrogen （H＋） ion beam irradiation. Ag-NWs are irradiated under H＋ ion beam at different ion fluences at room temperature. The Ag-NW network is fabricated by H＋ ion beam-induced welding of Ag-NWs at intersecting positions. H＋ ion beam induced welding is confirmed by transmission electron microscopy （TEM） and scanning electron microscopy （SEM）. Moreover, the structure of Ag NWs remains stable under H＋ ion beam, and networks are optically transparent. Morphology also remains stable under H＋ ion beam irradiation. No slicings or cuttings of Ag-NWs are observed under MeV H＋ ion beam irradiation. The results exhibit that the formation of Ag-NW network proceeds through three steps： ion beam induced thermal spikes lead to the local heating of Ag-NWs, the formation of simple junctions on small scale, and the formation of a large scale network. This observation is useful for using Ag-NWs based devices in upper space where protons are abandoned in an energy range from MeV to GeV. This high-quality Ag-NW network can also be used as a transparent electrode for optoelectronics devices.
文摘为了准确判断电池可用容量,采用长短期记忆神经网络对电池容量进行估算。首先分析电池各参数全生命周期变化曲线,计算其与电池容量之间的皮尔逊相关系数,选择电池电压、内阻、等压降时间等参数作为健康因子构建电池容量估计模型。使用美国先进寿命周期工程中心CALCE(Center for Advanced Life Cycle Engineering)电池数据集进行模型训练并估算电池容量,估计模型的平均百分误差为1.19%。分析估算误差产生的原因,通过电池初始容量参数修正和电池老化参数修正进行模型优化。优化结果表明,使用电池电压、内阻、恒流充电时间和4.0~3.4 V等压降时间构建模型估计误差在0.55%左右。
文摘锂离子电池荷电状态(state of charge,SOC)的精确估计对储能系统及电动汽车能源管理至关重要。为解决现有单一神经网络架构在复杂工况下的SOC估计精度不足问题,提出一种基于卷积历史序列分解混合(convolutional past decomposable mixing,CPDM)-长短期记忆(long short-term memory,LSTM)网络的混合估计模型。首先,通过平均池化方法与一维卷积神经网络对电池数据构建并提取多尺度时序特征;其次,利用CPDM模块对序列进行跨尺度分解与混合,以增强信息互补;最后,将增强的多尺度序列并行输入LSTM网络进行预测,并通过等权相加各尺度预测值得到SOC估计结果。实验结果表明,CPDM-LSTM模型在公开数据集上的SOC估计性能良好。其在不同温度及工况下的平均均方根误差为0.0485,平均绝对误差为0.0371,验证了模型较强的鲁棒性和泛化能力。
基金This work was financially supported by the Program of National Natural Science Foundation of China(No.50473031).
文摘By incorporating copper sulfate （CuSO4） particles into acrylonitrile butadiene rubber （NBR） followed by heat pressing, a novel vulcanization method is developed in rubber through the formation of coordination crosslinking. This method totally differs from traditional covalent or non-covalent vulcanization approaches of rubber. No other vulcanizing agent or additional additive is involved in this process. By analyzing the results of DMA, XPS and FT-IR, it is found that the crosslinking of CuSO4 particles filled NBR was induced by in situ coordination between nitrogen atoms of nitrile groups （-CN） and copper ions （Cu^2＋） from CuSO4. SEM and EDX results revealed the generation of a core （CuSO4 solid particle）- shell （adherent NBR） structure, which leads to a result that the crosslinked rubber has excellent mechanical properties. Moreover, poly（vinyl chloride） （PVC） and liquid acrylonitrile-butadiene rubber （LNBR） were used as mobilizer to improve the coordination crosslinking of CuSO4/NBR. The addition of PVC or LNBR could lead to higher crosslink density and better mechanical properties of coordination vulcanization. In addition, crystal water in CuSO4 played a positive role to coordination crosslinking of rubber because it decreased the metal point of CuSO4 and promoted the metal ionization.
