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
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
In this manuscript, we have demonstrated the delicate design and synthesis of bimetallic oxides nanoparticles derived from metal–oleate complex embedded in 3D graphene networks(MnO/CoMn_2O_4 GN), as an anode mater...In this manuscript, we have demonstrated the delicate design and synthesis of bimetallic oxides nanoparticles derived from metal–oleate complex embedded in 3D graphene networks(MnO/CoMn_2O_4 GN), as an anode material for lithium ion batteries. The novel synthesis of the MnO/CoMn_2O_4 GN consists of thermal decomposition of metal–oleate complex containing cobalt and manganese metals and oleate ligand, forming bimetallic oxides nanoparticles, followed by a selfassembly route with reduced graphene oxides. The MnO/CoMn_2O_4 GN composite, with a unique architecture of bimetallic oxides nanoparticles encapsulated in 3D graphene networks, rationally integrates several benefits including shortening the di usion path of Li^+ ions, improving electrical conductivity and mitigating volume variation during cycling. Studies show that the electrochemical reaction processes of MnO/Co Mn_2O_4 GN electrodes are dominated by the pseudocapacitive behavior, leading to fast Li^+ charge/discharge reactions. As a result, the MnO/CoMn_2O_4 GN manifests high initial specific capacity, stable cycling performance, and excellent rate capability.展开更多
The reversible transfer of unknown quantum states between light and matter is essential for constructing large-scale quantum networks. Over the last decade, various physical systems have been proposed to realize such ...The reversible transfer of unknown quantum states between light and matter is essential for constructing large-scale quantum networks. Over the last decade, various physical systems have been proposed to realize such quantum memory for light. The solid-state quantum memory based on rare-earth-ion-doped solids has the advantages of a reduced setup complexity and high robustness for scalable application. We describe the methods used to spectrally prepare the quantum memory and release the photonic excitation on-demand. We will review the state of the art experiments and discuss the perspective applications of this particular system in both quantum information science and fundamental tests of quantum physics.展开更多
电池健康状态(State of Health,SOH)和剩余使用寿命(Remaining Useful Life,RUL)是电池健康管理的重要评价指标。针对锂电池在使用过程中受较多复杂因素影响难以准确预测其剩余使用寿命问题,文中提出了一种基于IDBO-CNN-BiLSTM(Improved...电池健康状态(State of Health,SOH)和剩余使用寿命(Remaining Useful Life,RUL)是电池健康管理的重要评价指标。针对锂电池在使用过程中受较多复杂因素影响难以准确预测其剩余使用寿命问题,文中提出了一种基于IDBO-CNN-BiLSTM(Improved Dung Beetle Optimizer-Convolutional Neural Networks-Bi-directional Long Short-Term Memory)的混合预测模型。通过分析锂电池充电过程中的状态来提取9种健康因子(Health Factor,HF),通过皮尔逊相关系数筛选强相关性健康因子,并将其作为模型输入。采用混沌初始化Tent映射生成蜣螂的初始位置,采用正余弦策略优化偷窃蜣螂位置,解决了DBO(Dung Beetle Optimizer)算法初始化导致的局部收敛问题以及优化了DBO算法的平衡性,提高了预测的稳定性。基于NASA(National Aeronautics and Space Administration)提供的公开锂电池老化数据集进行实验,并使用不同模型预测NASA锂电池SOH,结果表明所提方法误差更小,具有一定应用价值。展开更多
Because a brain consists of tremendous neuronal networks with different neuron numbers ranging from tens to tens of thousands, we study the coherence resonance due to ion channel noises in globally coupled neuronal ne...Because a brain consists of tremendous neuronal networks with different neuron numbers ranging from tens to tens of thousands, we study the coherence resonance due to ion channel noises in globally coupled neuronal networks with different neuron numbers. We confirm that for all neuronal networks with different neuron numbers there exist the array enhanced coherence resonance and the optimal synaptic conductance to cause the maximal spiking coherence. Furthermoremore, the enhancement effects of coupling on spiking coherence and on optimal synaptic conductance are almost the same, regardless of the neuron numbers in the neuronal networks. Therefore for all the neuronal networks with different neuron numbers in the brain, relative weak synaptic conductance （0.1 mS/cm2） is sufficient to induce the maximal spiking coherence and the best sub-threshold signal encoding.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。
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.
基金financial support from National Natural Science Foundation of China (No. 21373006 and No. 51801030)the Science and Technology Program of Suzhou (SYG201732)+4 种基金the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD)the project of Scientific and Technologic Infrastructure of Suzhou (SZS201708)the Natural Science Foundation of the Jiangsu Higher Education Institutions of China (17KJB430029)One-hundred Young Talents (Class A) of Guangdong University of Technology (No. 220413198)Natural Science Foundation of Guangdong Providence (No. 2018A030310571)
文摘In this manuscript, we have demonstrated the delicate design and synthesis of bimetallic oxides nanoparticles derived from metal–oleate complex embedded in 3D graphene networks(MnO/CoMn_2O_4 GN), as an anode material for lithium ion batteries. The novel synthesis of the MnO/CoMn_2O_4 GN consists of thermal decomposition of metal–oleate complex containing cobalt and manganese metals and oleate ligand, forming bimetallic oxides nanoparticles, followed by a selfassembly route with reduced graphene oxides. The MnO/CoMn_2O_4 GN composite, with a unique architecture of bimetallic oxides nanoparticles encapsulated in 3D graphene networks, rationally integrates several benefits including shortening the di usion path of Li^+ ions, improving electrical conductivity and mitigating volume variation during cycling. Studies show that the electrochemical reaction processes of MnO/Co Mn_2O_4 GN electrodes are dominated by the pseudocapacitive behavior, leading to fast Li^+ charge/discharge reactions. As a result, the MnO/CoMn_2O_4 GN manifests high initial specific capacity, stable cycling performance, and excellent rate capability.
基金Project supported by the National Key Research and Development Program of China(Grant No.2017YFA0304100)the National Natural Science Foundation of China(Grant Nos.61327901,11774331,11774335,11504362,11325419,and 11654002)+1 种基金the Key Research Program of Frontier Sciences,Chinese Academy of Sciences(Grant No.QYZDY-SSW-SLH003)the Fundamental Research Funds for the Central Universities,China(Grant Nos.WK2470000023 and WK2470000026)
文摘The reversible transfer of unknown quantum states between light and matter is essential for constructing large-scale quantum networks. Over the last decade, various physical systems have been proposed to realize such quantum memory for light. The solid-state quantum memory based on rare-earth-ion-doped solids has the advantages of a reduced setup complexity and high robustness for scalable application. We describe the methods used to spectrally prepare the quantum memory and release the photonic excitation on-demand. We will review the state of the art experiments and discuss the perspective applications of this particular system in both quantum information science and fundamental tests of quantum physics.
文摘电池健康状态(State of Health,SOH)和剩余使用寿命(Remaining Useful Life,RUL)是电池健康管理的重要评价指标。针对锂电池在使用过程中受较多复杂因素影响难以准确预测其剩余使用寿命问题,文中提出了一种基于IDBO-CNN-BiLSTM(Improved Dung Beetle Optimizer-Convolutional Neural Networks-Bi-directional Long Short-Term Memory)的混合预测模型。通过分析锂电池充电过程中的状态来提取9种健康因子(Health Factor,HF),通过皮尔逊相关系数筛选强相关性健康因子,并将其作为模型输入。采用混沌初始化Tent映射生成蜣螂的初始位置,采用正余弦策略优化偷窃蜣螂位置,解决了DBO(Dung Beetle Optimizer)算法初始化导致的局部收敛问题以及优化了DBO算法的平衡性,提高了预测的稳定性。基于NASA(National Aeronautics and Space Administration)提供的公开锂电池老化数据集进行实验,并使用不同模型预测NASA锂电池SOH,结果表明所提方法误差更小,具有一定应用价值。
基金supported by the National Natural Science Foundation of China (Grant No.11065003)the Natural Science Foundation of Guangxi Zhuang Autonoomous Region,China (Grant No.2011GXNSFA018129)the Research Funding of Education Department of Guangxi Zhuang Autonoomous Region of China (Grant No.201012MS026)
文摘Because a brain consists of tremendous neuronal networks with different neuron numbers ranging from tens to tens of thousands, we study the coherence resonance due to ion channel noises in globally coupled neuronal networks with different neuron numbers. We confirm that for all neuronal networks with different neuron numbers there exist the array enhanced coherence resonance and the optimal synaptic conductance to cause the maximal spiking coherence. Furthermoremore, the enhancement effects of coupling on spiking coherence and on optimal synaptic conductance are almost the same, regardless of the neuron numbers in the neuronal networks. Therefore for all the neuronal networks with different neuron numbers in the brain, relative weak synaptic conductance （0.1 mS/cm2） is sufficient to induce the maximal spiking coherence and the best sub-threshold signal encoding.
