Manufacturing process,diffusion co-efficient and areal capacity are the three main criteria for regulating thick electrodes for lithium-ion batteries(LIBs).However,simultaneously regulating these criteria for LIBs is ...Manufacturing process,diffusion co-efficient and areal capacity are the three main criteria for regulating thick electrodes for lithium-ion batteries(LIBs).However,simultaneously regulating these criteria for LIBs is desirable but remains a significant challenge.In this work,niobium pentoxide(Nb_(2)O_(5))anode and lithium iron phosphate(LiFePO_(4))cathode materials were chosen as the model materials and demonstrate that these three parameters can be simultaneously modulated by incorporation of micro-carbon fibers(MCF)and carbon nanotubes(CNT)with both Nb_(2)O_(5) and LFP via vacuum filtration approach.Both as-prepared MNC-20 anode and MLC-20 cathode achieves high reversible areal capacity of≈5.4 m A h cm^(-2)@0.1 C and outstanding Li-ion diffusion coefficients of≈10~(-8)cm~2 s~(-1)in the half-cell configuration.The assembled MNC-20‖MLC-20 full cell LIB delivers maximum energy and power densities of244.04 W h kg^(-1)and 108.86 W kg^(-1),respectively.The excellent electrochemical properties of the asprepared thick electrodes can be attributed to the highly conductive,mechanical compactness and multidimensional mutual effects of the MCF,CNT and active materials that facilitates rapid Li-ion diffusion kinetics.Furthermore,electrochemical impedance spectroscopy(EIS),symmetric cells analysis,and insitu Raman techniques clearly validates the enhanced Li-ion diffusion kinetics in the present architecture.展开更多
The solid electrolyte interphase(SEI),a passivation film covering the electrode surface,is crucial to the lifetime and efficiency of the lithium-ion(Li-ion)battery.Understanding the Li-ion diffusion mechanism within p...The solid electrolyte interphase(SEI),a passivation film covering the electrode surface,is crucial to the lifetime and efficiency of the lithium-ion(Li-ion)battery.Understanding the Li-ion diffusion mechanism within possible components in the mosaic-structured SEI is an essential step to improve the Li-ion conductivity and thus the battery performance.Here,we investigate the Li-ion diffusion mechanism within three amorphous SEI components(i.e.,the inorganic inner layer,organic outer layer,and their mixture with 1:1 molar ratio)via ab initio molecular dynamic(AIMD)simulations.Our simulations show that the Li-ion diffusion coefficient in the inorganic layer is two orders of magnitude faster than that in the organic layer.Therefore,the inorganic layer makes a major contribution to the Li-ion diffusion.Furthermore,we find that the Li-ion diffusivity in the organic layer decreases slightly with the increase of the carbon chain from the methyl to ethyl owing to the steric hindrance induced by large groups.Overall,our current work unravels the Li-ion diffusion mechanism,and provides an atomic-scale insight for the understanding of the Li-ion transport in the SEI components.展开更多
Progressive delamination driven by Li-ion diffusion in elastic disk-like thin film electrodes of Li-ion batteries is modeled based on the cohesive model. Axisymmetric diffusion model is considered under both galvanost...Progressive delamination driven by Li-ion diffusion in elastic disk-like thin film electrodes of Li-ion batteries is modeled based on the cohesive model. Axisymmetric diffusion model is considered under both galvanostatic and potentiostatic operations. The effect of edge diffusion on the delamination process is evaluated. It is found that the diffusion from edge leads to an earlier delamination initiation. The edge effect is significant for active disks with a small aspect ratio, but negligible for the case of large aspect ratio. The edge diffusion is weaker in the potentiostatic operation than in the galvanostatic operation.展开更多
Human motion modeling is a core technology in computer animation,game development,and humancomputer interaction.In particular,generating natural and coherent in-between motion using only the initial and terminal frame...Human motion modeling is a core technology in computer animation,game development,and humancomputer interaction.In particular,generating natural and coherent in-between motion using only the initial and terminal frames remains a fundamental yet unresolved challenge.Existing methods typically rely on dense keyframe inputs or complex prior structures,making it difficult to balance motion quality and plausibility under conditions such as sparse constraints,long-term dependencies,and diverse motion styles.To address this,we propose a motion generation framework based on a frequency-domain diffusion model,which aims to better model complex motion distributions and enhance generation stability under sparse conditions.Our method maps motion sequences to the frequency domain via the Discrete Cosine Transform(DCT),enabling more effective modeling of low-frequency motion structures while suppressing high-frequency noise.A denoising network based on self-attention is introduced to capture long-range temporal dependencies and improve global structural awareness.Additionally,a multi-objective loss function is employed to jointly optimize motion smoothness,pose diversity,and anatomical consistency,enhancing the realism and physical plausibility of the generated sequences.Comparative experiments on the Human3.6M and LaFAN1 datasets demonstrate that our method outperforms state-of-the-art approaches across multiple performance metrics,showing stronger capabilities in generating intermediate motion frames.This research offers a new perspective and methodology for human motion generation and holds promise for applications in character animation,game development,and virtual interaction.展开更多
Missing values in radionuclide diffusion datasets can undermine the predictive accuracy and robustness of the machine learning(ML)models.In this study,regression-based missing data imputation method using a light grad...Missing values in radionuclide diffusion datasets can undermine the predictive accuracy and robustness of the machine learning(ML)models.In this study,regression-based missing data imputation method using a light gradient boosting machine(LGBM)algorithm was employed to impute more than 60%of the missing data,establishing a radionuclide diffusion dataset containing 16 input features and 813 instances.The effective diffusion coefficient(D_(e))was predicted using ten ML models.The predictive accuracy of the ensemble meta-models,namely LGBM-extreme gradient boosting(XGB)and LGBM-categorical boosting(CatB),surpassed that of the other ML models,with R^(2)values of 0.94.The models were applied to predict the D_(e)values of EuEDTA^(−)and HCrO_(4)^(−)in saturated compacted bentonites at compactions ranging from 1200 to 1800 kg/m^(3),which were measured using a through-diffusion method.The generalization ability of the LGBM-XGB model surpassed that of LGB-CatB in predicting the D_(e)of HCrO_(4)^(−).Shapley additive explanations identified total porosity as the most significant influencing factor.Additionally,the partial dependence plot analysis technique yielded clearer results in the univariate correlation analysis.This study provides a regression imputation technique to refine radionuclide diffusion datasets,offering deeper insights into analyzing the diffusion mechanism of radionuclides and supporting the safety assessment of the geological disposal of high-level radioactive waste.展开更多
A medical image encryption is proposed based on the Fisher-Yates scrambling,filter diffusion and S-box substitution.First,chaotic sequence associated with the plaintext is generated by logistic-sine-cosine system,whic...A medical image encryption is proposed based on the Fisher-Yates scrambling,filter diffusion and S-box substitution.First,chaotic sequence associated with the plaintext is generated by logistic-sine-cosine system,which is used for the scrambling,substitution and diffusion processes.The three-dimensional Fisher-Yates scrambling,S-box substitution and diffusion are employed for the first round of encryption.The chaotic sequence is adopted for secondary encryption to scramble the ciphertext obtained in the first round.Then,three-dimensional filter is applied to diffusion for further useful information hiding.The key to the algorithm is generated by the combination of hash value of plaintext image and the input parameters.It improves resisting ability of plaintext attacks.The security analysis shows that the algorithm is effective and efficient.It can resist common attacks.In addition,the good diffusion effect shows that the scheme can solve the differential attacks encountered in the transmission of medical images and has positive implications for future research.展开更多
The dynamics of phase separation in H–He binary systems within gas giants such as Jupiter and Saturn exhibit remarkable complexity, yet lack systematic investigation. Through large-scale machine-learning-accelerated ...The dynamics of phase separation in H–He binary systems within gas giants such as Jupiter and Saturn exhibit remarkable complexity, yet lack systematic investigation. Through large-scale machine-learning-accelerated molecular dynamics simulations spanning broad temperature-pressure-composition(2000–10000 K, 1–7 Mbar,pure H to pure He) regimes, we systematically determine self and mutual diffusion coefficients in H–He systems and establish a six-dimensional framework correlating temperature, pressure, helium abundance, phase separation degree, diffusion coefficients, and anisotropy. Key findings reveal that hydrogen exhibits active directional migration with pronounced diffusion anisotropy, whereas helium passively aggregates in response. While the conventional mixing rule underestimates mutual diffusion coefficients by neglecting velocity cross-correlations,the assumption of an ideal thermodynamic factor(Q = 1) overestimates them due to unaccounted non-ideal thermodynamic effects—both particularly pronounced in strongly phase-separated regimes. Notably, hydrogen's dual role, anisotropic diffusion and bond stabilization via helium doping, modulates demixing kinetics. Large-scale simulations(216,000 atoms) propose novel phase-separation paradigms, such as “hydrogen bubble/wisp” formation, challenging the classical “helium rain” scenario, striving to bridge atomic-scale dynamics to planetary-scale phase evolution.展开更多
The interdiffusion coefficients in Al_(0.2)CoCrFeNi,CoCrCu_(0.2)FeNi,and CoCrFeMn_(0.2)Ni high-entropy alloys were efficiently determined by combining diffusion couple experiments and high-throughput determination of ...The interdiffusion coefficients in Al_(0.2)CoCrFeNi,CoCrCu_(0.2)FeNi,and CoCrFeMn_(0.2)Ni high-entropy alloys were efficiently determined by combining diffusion couple experiments and high-throughput determination of interdiffusion coefficients(HitDIC)software at 1273−1373 K.The results show that the addition of Al,Cu,and Mn to CoCrFeNi high-entropy alloys promotes the diffusion of Co,Cr,and Fe atoms.The comparison of tracer diffusion coefficients indicates that there is no sluggish diffusion in tracer diffusion on the thermodynamic temperature scale for the present Al_(0.2)CoCrFeNi,CoCrCu_(0.2)FeNi,and CoCrFeMn_(0.2)Ni high-entropy alloys.The linear relationship between diffusion entropy and activation energy reveals that the diffusion process of atoms is unaffected by an increase in the number of components as long as the crystal structure remains unchanged.展开更多
A new model of porous electrodes based on the Gibbs free energy is developed, in which lithium-ion(Liion) diffusion, diffusion-induced stress(DIS), Butler–Volmer(BV) reaction kinetics, and size polydispersity of elec...A new model of porous electrodes based on the Gibbs free energy is developed, in which lithium-ion(Liion) diffusion, diffusion-induced stress(DIS), Butler–Volmer(BV) reaction kinetics, and size polydispersity of electrode particles are considered. The influence of BV reaction kinetics and concentration-dependent exchange current density(ECD) on concentration profile and DIS evolution are numerically investigated. BV reaction kinetics leads to a decrease in Li-ion concentration and DIS. In addition, concentrationdependent ECD results in a decrease in Li-ion concentration and an increase in DIS. Size polydispersity of electrode particles significantly affects the concentration profile and DIS.Optimal macroscopic state of charge(SOC) should consider the influence of the microscopic SOC values and mass fractions of differently sized particles.展开更多
Grain boundary diffusion technology is pivotal in the preparation of high-performance NdFeB magnets.This study investigates the factors that affect the efficiency of grain boundary diffusion,starting from the properti...Grain boundary diffusion technology is pivotal in the preparation of high-performance NdFeB magnets.This study investigates the factors that affect the efficiency of grain boundary diffusion,starting from the properties of the diffusion matrix.Through the adjustment of the sintering process,we effectively prepared magnets with varied densities that serve as the matrix for grain boundary diffusion with TbH,diffusion.The mobility characteristics of the Nd-rich phase during the densification stage are leveraged to ensure a more extensive distribution of heavy rare earth elements within the magnets.According to the experimental results,the increase in coercivity of low-density magnets after diffusion is significantly greater than that of relatively high-density magnets.The coercivity values measured are 805.32 kA/m for low-density magnets and 470.3 kA/m for high-density magnets.Additionally,grain boundary diffusion notably enhances the density of initial low-density magnets,addressing the issue of low density during the sintering stage.Before the diffusion treatment,the Nd-rich phases primarily concentrate at the triangular grain boundaries,resulting in an increased number of cavity defects in the magnets.These cavity defects contain atoms in a higher energy state,making them more prone to transition.Consequently,the diffusion activation energy at the void defects is lower than the intracrystalline diffusion activation energy,accelerating atom diffusion.The presence of larger cavities also provides more space for atom migration,thereby promoting the diffusion process.After the diffusion treatment,the proportion of bulk Nd-rich phases significantly decreases,and they infiltrate between the grains to fill the cavity defects,forming continuous fine grain boundaries.Based on these observations,the study aims to explore how to utilize this information to develop an efficient technique for grain boundary diffusion.展开更多
Imputation of missing data has long been an important topic and an essential application for intelligent transportation systems(ITS)in the real world.As a state-of-the-art generative model,the diffusion model has prov...Imputation of missing data has long been an important topic and an essential application for intelligent transportation systems(ITS)in the real world.As a state-of-the-art generative model,the diffusion model has proven highly successful in image generation,speech generation,time series modelling etc.and now opens a new avenue for traffic data imputation.In this paper,we propose a conditional diffusion model,called the implicit-explicit diffusion model,for traffic data imputation.This model exploits both the implicit and explicit feature of the data simultaneously.More specifically,we design two types of feature extraction modules,one to capture the implicit dependencies hidden in the raw data at multiple time scales and the other to obtain the long-term temporal dependencies of the time series.This approach not only inherits the advantages of the diffusion model for estimating missing data,but also takes into account the multiscale correlation inherent in traffic data.To illustrate the performance of the model,extensive experiments are conducted on three real-world time series datasets using different missing rates.The experimental results demonstrate that the model improves imputation accuracy and generalization capability.展开更多
基金supported by the Science and Technology Innovation Program of Hunan Province(2022WZ1012)the Hunan Joint International Laboratory of Advanced Materials and Technology for Clean Energy(2020CB1007)the Natural Science Foundation of Guangzhou(202201020147)。
文摘Manufacturing process,diffusion co-efficient and areal capacity are the three main criteria for regulating thick electrodes for lithium-ion batteries(LIBs).However,simultaneously regulating these criteria for LIBs is desirable but remains a significant challenge.In this work,niobium pentoxide(Nb_(2)O_(5))anode and lithium iron phosphate(LiFePO_(4))cathode materials were chosen as the model materials and demonstrate that these three parameters can be simultaneously modulated by incorporation of micro-carbon fibers(MCF)and carbon nanotubes(CNT)with both Nb_(2)O_(5) and LFP via vacuum filtration approach.Both as-prepared MNC-20 anode and MLC-20 cathode achieves high reversible areal capacity of≈5.4 m A h cm^(-2)@0.1 C and outstanding Li-ion diffusion coefficients of≈10~(-8)cm~2 s~(-1)in the half-cell configuration.The assembled MNC-20‖MLC-20 full cell LIB delivers maximum energy and power densities of244.04 W h kg^(-1)and 108.86 W kg^(-1),respectively.The excellent electrochemical properties of the asprepared thick electrodes can be attributed to the highly conductive,mechanical compactness and multidimensional mutual effects of the MCF,CNT and active materials that facilitates rapid Li-ion diffusion kinetics.Furthermore,electrochemical impedance spectroscopy(EIS),symmetric cells analysis,and insitu Raman techniques clearly validates the enhanced Li-ion diffusion kinetics in the present architecture.
基金R.Wen acknowledges the financial support from the National Key R&D Program of China(No.2021YFB2500300)the CAS Project for Young Scientists in Basic Research(No.YSBR-058)+2 种基金S.Xu acknowledges funding support from the Chinese Ministry of Science and Technology(No.2021YFB3800303)DP Technology Corporation(No.2021110016001141)the School of Materials Science and Engineering at Peking University.
文摘The solid electrolyte interphase(SEI),a passivation film covering the electrode surface,is crucial to the lifetime and efficiency of the lithium-ion(Li-ion)battery.Understanding the Li-ion diffusion mechanism within possible components in the mosaic-structured SEI is an essential step to improve the Li-ion conductivity and thus the battery performance.Here,we investigate the Li-ion diffusion mechanism within three amorphous SEI components(i.e.,the inorganic inner layer,organic outer layer,and their mixture with 1:1 molar ratio)via ab initio molecular dynamic(AIMD)simulations.Our simulations show that the Li-ion diffusion coefficient in the inorganic layer is two orders of magnitude faster than that in the organic layer.Therefore,the inorganic layer makes a major contribution to the Li-ion diffusion.Furthermore,we find that the Li-ion diffusivity in the organic layer decreases slightly with the increase of the carbon chain from the methyl to ethyl owing to the steric hindrance induced by large groups.Overall,our current work unravels the Li-ion diffusion mechanism,and provides an atomic-scale insight for the understanding of the Li-ion transport in the SEI components.
基金supported by the National Science Foundation of China (11102103 and 11172159)the Shanghai Municipal Education Commission, China (13ZZ070)+1 种基金the Graduate School of Shanghai University (SHUCX120123)the Science and Technology Commission of Shanghai Municipality, China(12ZR1410200)
文摘Progressive delamination driven by Li-ion diffusion in elastic disk-like thin film electrodes of Li-ion batteries is modeled based on the cohesive model. Axisymmetric diffusion model is considered under both galvanostatic and potentiostatic operations. The effect of edge diffusion on the delamination process is evaluated. It is found that the diffusion from edge leads to an earlier delamination initiation. The edge effect is significant for active disks with a small aspect ratio, but negligible for the case of large aspect ratio. The edge diffusion is weaker in the potentiostatic operation than in the galvanostatic operation.
基金supported by the National Natural Science Foundation of China(Grant No.72161034).
文摘Human motion modeling is a core technology in computer animation,game development,and humancomputer interaction.In particular,generating natural and coherent in-between motion using only the initial and terminal frames remains a fundamental yet unresolved challenge.Existing methods typically rely on dense keyframe inputs or complex prior structures,making it difficult to balance motion quality and plausibility under conditions such as sparse constraints,long-term dependencies,and diverse motion styles.To address this,we propose a motion generation framework based on a frequency-domain diffusion model,which aims to better model complex motion distributions and enhance generation stability under sparse conditions.Our method maps motion sequences to the frequency domain via the Discrete Cosine Transform(DCT),enabling more effective modeling of low-frequency motion structures while suppressing high-frequency noise.A denoising network based on self-attention is introduced to capture long-range temporal dependencies and improve global structural awareness.Additionally,a multi-objective loss function is employed to jointly optimize motion smoothness,pose diversity,and anatomical consistency,enhancing the realism and physical plausibility of the generated sequences.Comparative experiments on the Human3.6M and LaFAN1 datasets demonstrate that our method outperforms state-of-the-art approaches across multiple performance metrics,showing stronger capabilities in generating intermediate motion frames.This research offers a new perspective and methodology for human motion generation and holds promise for applications in character animation,game development,and virtual interaction.
基金supported by the National Natural Science Foundation of China(No.12475340 and 12375350)Special Branch project of South Taihu Lakethe Scientific Research Fund of Zhejiang Provincial Education Department(No.Y202456326).
文摘Missing values in radionuclide diffusion datasets can undermine the predictive accuracy and robustness of the machine learning(ML)models.In this study,regression-based missing data imputation method using a light gradient boosting machine(LGBM)algorithm was employed to impute more than 60%of the missing data,establishing a radionuclide diffusion dataset containing 16 input features and 813 instances.The effective diffusion coefficient(D_(e))was predicted using ten ML models.The predictive accuracy of the ensemble meta-models,namely LGBM-extreme gradient boosting(XGB)and LGBM-categorical boosting(CatB),surpassed that of the other ML models,with R^(2)values of 0.94.The models were applied to predict the D_(e)values of EuEDTA^(−)and HCrO_(4)^(−)in saturated compacted bentonites at compactions ranging from 1200 to 1800 kg/m^(3),which were measured using a through-diffusion method.The generalization ability of the LGBM-XGB model surpassed that of LGB-CatB in predicting the D_(e)of HCrO_(4)^(−).Shapley additive explanations identified total porosity as the most significant influencing factor.Additionally,the partial dependence plot analysis technique yielded clearer results in the univariate correlation analysis.This study provides a regression imputation technique to refine radionuclide diffusion datasets,offering deeper insights into analyzing the diffusion mechanism of radionuclides and supporting the safety assessment of the geological disposal of high-level radioactive waste.
文摘A medical image encryption is proposed based on the Fisher-Yates scrambling,filter diffusion and S-box substitution.First,chaotic sequence associated with the plaintext is generated by logistic-sine-cosine system,which is used for the scrambling,substitution and diffusion processes.The three-dimensional Fisher-Yates scrambling,S-box substitution and diffusion are employed for the first round of encryption.The chaotic sequence is adopted for secondary encryption to scramble the ciphertext obtained in the first round.Then,three-dimensional filter is applied to diffusion for further useful information hiding.The key to the algorithm is generated by the combination of hash value of plaintext image and the input parameters.It improves resisting ability of plaintext attacks.The security analysis shows that the algorithm is effective and efficient.It can resist common attacks.In addition,the good diffusion effect shows that the scheme can solve the differential attacks encountered in the transmission of medical images and has positive implications for future research.
基金supported by the National University of Defense Technology Research Fund Projectthe National Natural Science Foundation of China under Grant Nos. 12047561 and 12104507+1 种基金the NSAF under Grant No. U1830206the Science and Technology Innovation Program of Hunan Province under Grant No. 2021RC4026。
文摘The dynamics of phase separation in H–He binary systems within gas giants such as Jupiter and Saturn exhibit remarkable complexity, yet lack systematic investigation. Through large-scale machine-learning-accelerated molecular dynamics simulations spanning broad temperature-pressure-composition(2000–10000 K, 1–7 Mbar,pure H to pure He) regimes, we systematically determine self and mutual diffusion coefficients in H–He systems and establish a six-dimensional framework correlating temperature, pressure, helium abundance, phase separation degree, diffusion coefficients, and anisotropy. Key findings reveal that hydrogen exhibits active directional migration with pronounced diffusion anisotropy, whereas helium passively aggregates in response. While the conventional mixing rule underestimates mutual diffusion coefficients by neglecting velocity cross-correlations,the assumption of an ideal thermodynamic factor(Q = 1) overestimates them due to unaccounted non-ideal thermodynamic effects—both particularly pronounced in strongly phase-separated regimes. Notably, hydrogen's dual role, anisotropic diffusion and bond stabilization via helium doping, modulates demixing kinetics. Large-scale simulations(216,000 atoms) propose novel phase-separation paradigms, such as “hydrogen bubble/wisp” formation, challenging the classical “helium rain” scenario, striving to bridge atomic-scale dynamics to planetary-scale phase evolution.
基金supported by the National Natural Science Foundation of China(No.52374372)the Natural Science Foundation of the Jiangsu Higher Education Institutions of China(No.23KJB430042)+3 种基金the Jiangsu Province Large Scientific Instruments Open Sharing Autonomous Research Filing Project,China(No.TC2023A037)the Yangzhou City−Yangzhou University Cooperation Foundation,China(No.YZ2022183)High-end Talent Support Program of Yangzhou University,China,Qinglan Project of Yangzhou University,ChinaLvyangjinfeng Talent program of Yangzhou,China.
文摘The interdiffusion coefficients in Al_(0.2)CoCrFeNi,CoCrCu_(0.2)FeNi,and CoCrFeMn_(0.2)Ni high-entropy alloys were efficiently determined by combining diffusion couple experiments and high-throughput determination of interdiffusion coefficients(HitDIC)software at 1273−1373 K.The results show that the addition of Al,Cu,and Mn to CoCrFeNi high-entropy alloys promotes the diffusion of Co,Cr,and Fe atoms.The comparison of tracer diffusion coefficients indicates that there is no sluggish diffusion in tracer diffusion on the thermodynamic temperature scale for the present Al_(0.2)CoCrFeNi,CoCrCu_(0.2)FeNi,and CoCrFeMn_(0.2)Ni high-entropy alloys.The linear relationship between diffusion entropy and activation energy reveals that the diffusion process of atoms is unaffected by an increase in the number of components as long as the crystal structure remains unchanged.
基金financial support by the National Natural Science Foundation of China (Grants 11472165, 11332005)
文摘A new model of porous electrodes based on the Gibbs free energy is developed, in which lithium-ion(Liion) diffusion, diffusion-induced stress(DIS), Butler–Volmer(BV) reaction kinetics, and size polydispersity of electrode particles are considered. The influence of BV reaction kinetics and concentration-dependent exchange current density(ECD) on concentration profile and DIS evolution are numerically investigated. BV reaction kinetics leads to a decrease in Li-ion concentration and DIS. In addition, concentrationdependent ECD results in a decrease in Li-ion concentration and an increase in DIS. Size polydispersity of electrode particles significantly affects the concentration profile and DIS.Optimal macroscopic state of charge(SOC) should consider the influence of the microscopic SOC values and mass fractions of differently sized particles.
基金Project supported by the National Natural Science Foundation of China(52361033)National Key Research and Development Program(2022YFB3505400)+3 种基金Ministry of Industry and Information Technology Heavy Rare Earth Special Use of Sintered NdFeB Project(TC220H06J)Academic and Technical Leaders in Major Disciplines in Jiangxi Province(2022BCJ23007)Jiangxi Province Science and Technology Cooperation Key Project(20212BDH80007)Jiangxi Graduate Student Innovation Special Fund Project(YC2023-B213)。
文摘Grain boundary diffusion technology is pivotal in the preparation of high-performance NdFeB magnets.This study investigates the factors that affect the efficiency of grain boundary diffusion,starting from the properties of the diffusion matrix.Through the adjustment of the sintering process,we effectively prepared magnets with varied densities that serve as the matrix for grain boundary diffusion with TbH,diffusion.The mobility characteristics of the Nd-rich phase during the densification stage are leveraged to ensure a more extensive distribution of heavy rare earth elements within the magnets.According to the experimental results,the increase in coercivity of low-density magnets after diffusion is significantly greater than that of relatively high-density magnets.The coercivity values measured are 805.32 kA/m for low-density magnets and 470.3 kA/m for high-density magnets.Additionally,grain boundary diffusion notably enhances the density of initial low-density magnets,addressing the issue of low density during the sintering stage.Before the diffusion treatment,the Nd-rich phases primarily concentrate at the triangular grain boundaries,resulting in an increased number of cavity defects in the magnets.These cavity defects contain atoms in a higher energy state,making them more prone to transition.Consequently,the diffusion activation energy at the void defects is lower than the intracrystalline diffusion activation energy,accelerating atom diffusion.The presence of larger cavities also provides more space for atom migration,thereby promoting the diffusion process.After the diffusion treatment,the proportion of bulk Nd-rich phases significantly decreases,and they infiltrate between the grains to fill the cavity defects,forming continuous fine grain boundaries.Based on these observations,the study aims to explore how to utilize this information to develop an efficient technique for grain boundary diffusion.
基金partially supported by the National Natural Science Foundation of China(62271485)the SDHS Science and Technology Project(HS2023B044)
文摘Imputation of missing data has long been an important topic and an essential application for intelligent transportation systems(ITS)in the real world.As a state-of-the-art generative model,the diffusion model has proven highly successful in image generation,speech generation,time series modelling etc.and now opens a new avenue for traffic data imputation.In this paper,we propose a conditional diffusion model,called the implicit-explicit diffusion model,for traffic data imputation.This model exploits both the implicit and explicit feature of the data simultaneously.More specifically,we design two types of feature extraction modules,one to capture the implicit dependencies hidden in the raw data at multiple time scales and the other to obtain the long-term temporal dependencies of the time series.This approach not only inherits the advantages of the diffusion model for estimating missing data,but also takes into account the multiscale correlation inherent in traffic data.To illustrate the performance of the model,extensive experiments are conducted on three real-world time series datasets using different missing rates.The experimental results demonstrate that the model improves imputation accuracy and generalization capability.
