Single-crystalline layered oxide materials for lithium-ion batteries are featured by their excellent capacity retention over their polycrystalline counterparts,making them sought-after cathode candidates.Their capacit...Single-crystalline layered oxide materials for lithium-ion batteries are featured by their excellent capacity retention over their polycrystalline counterparts,making them sought-after cathode candidates.Their capacity degradation,however,becomes more severe under high-voltage cycling,hindering many high-energy applications.It has long been speculated that the interplay among composition heterogeneity,lattice deformation,and redox stratification could be a driving force for the performance decay.The underlying mechanism,however,is not well-understood.In this study,we use X-ray microscopy to systematically examine single-crystalline NMC particles at the mesoscale.This technique allows us to capture detailed signals of diffraction,spectroscopy,and fluorescence,offering spatially resolved multimodal insights.Focusing on early high-voltage charging cycles,we uncover heterogeneities in valence states and lattice structures that are inherent rather than caused by electrochemical abuse.These heterogeneities are closely associated with compositional variations within individual particles.Our findings provide useful insights for refining material synthesis and processing for enhanced battery longevity and efficiency.展开更多
A long-standing challenge in tomography is the‘missing wedge’problem,which arises when the acquisition of projection images within a certain angular range is restricted due to geometrical constraints.This incomplete...A long-standing challenge in tomography is the‘missing wedge’problem,which arises when the acquisition of projection images within a certain angular range is restricted due to geometrical constraints.This incomplete dataset results in significant artifacts and poor resolution in the reconstructed image.To tackle this challenge,we propose an approach dubbed Perception Fused Iterative Tomography Reconstruction Engine,which integrates a convolutional neural network(CNN)with perceptional knowledge as a smart regularizer into an iterative solving engine.We employ the Alternating Direction Method of Multipliers to optimize the solution in both physics and image domains,thereby achieving a physically coherent and visually enhanced result.We demonstrate the effectiveness of the proposed approach using various experimental datasets obtained with different x-ray microscopy techniques.All show significantly improved reconstruction even with a missing wedge of over 100 degrees−a scenario where conventional methods fail.Notably,it also improves the reconstruction in case of sparse projections,despite the network not being specifically trained for that.This demonstrates the robustness and generality of our method of addressing commonly occurring challenges in 3D x-ray imaging applications for real-world problems.展开更多
Nanostructured silicon has generated significant excitement for use as the anode material for lithium-ion batteries; however, more effort is needed to produce nanostructured silicon in a scalable fashion and with good...Nanostructured silicon has generated significant excitement for use as the anode material for lithium-ion batteries; however, more effort is needed to produce nanostructured silicon in a scalable fashion and with good performance. Here, we present a direct preparation of porous silicon nanoparticles as a new kind of nanostructured silicon using a novel two-step approach combining controlled boron doping and facile electroless etching. The porous silicon nanoparticles have been successfully used as high performance lithium-ion battery anodes, with capacities around 1,400 mA.h/g achieved at a current rate of 1 A/g, and 1,000 mA.h/g achieved at 2 A/g, and stable operation when combined with reduced graphene oxide and tested over up to 200 cycles. We attribute the overall good performance to the combination of porous silicon that can accommodate large volume change during cycling and provide large surface area accessible to electrolyte, and reduced graphene oxide that can serve as an elastic and electrically conductive matrix for the porous silicon nanoparticles.展开更多
Silicon （Si） has the highest known theoretical specific capacity （3,590 mAh/g for Li1.5Si4, and 4,200 mAh/g for Li22Si4） as a lithium-ion battery anode, and has attracted extensive interest in the past few years. ...Silicon （Si） has the highest known theoretical specific capacity （3,590 mAh/g for Li1.5Si4, and 4,200 mAh/g for Li22Si4） as a lithium-ion battery anode, and has attracted extensive interest in the past few years. However, its application is limited by poor cyclability and early capacity fading due to significant volume changes during lithiation and delithiation processes. In this work, we report a coaxial silicon/anodic titanium oxide/silicon （Si-ATO--Si） nanotube array structure grown on a titanium substrate demonstrating excellent electrochemical cyclability. The ATO nanotube scaffold used for Si deposition has many desirable features, such as a rough surface for enhanced Si adhesion, and direct contact with the Ti substrate working as current collector. More importantly, our ATO scaffold provides a rather unique advantage in that Si can be loaded on both the inner and outer surfaces, and an inner pore can be retained to provide room for Si volume expansion. This coaxial structure shows a capacity above 1,500 mAh/g after 100 cycles, with less than 0.05% decay per cycle. Simulations show that this improved performance can be attributed to the lower stress induced on Si layers upon lithiation/delithiation compared with some other recently reported Si-based nanostructures.展开更多
Improving the spatial and spectral resolution of 2D X-ray near-edge absorption structure(XANES)has been a decade-long pursuit to probe local chemical reactions at the nanoscale.However,the poor signal-to-noise ratio i...Improving the spatial and spectral resolution of 2D X-ray near-edge absorption structure(XANES)has been a decade-long pursuit to probe local chemical reactions at the nanoscale.However,the poor signal-to-noise ratio in the measured images poses significant challenges in quantitative analysis,especially when the element of interest is at a low concentration.In this work,we developed a postimaging processing method using deep neural network to reliably improve the signal-to-noise ratio in the XANES images.展开更多
基金This research used resources 3-ID Hard x-ray nano probe and 18-ID full field x-ray imaging of the National Synchrotron Light Source IIa U.S.Department of Energy(DOE)Office of Science User Facility operated for the DOE Office of Science by Brookhaven National Laboratory under Contract No.DE-SC0012704+2 种基金Stanford Synchrotron Radiation Lightsource of the SLAC National Accelerator Laboratory is supported by the U.S.Department of Energy,Office of Science,Office of Basic Energy Sciences under Contract No.DE-AC02-76SF00515The work at the Central Universities of Central South University was sponsored by the National Natural Science Foundation of China(52172264)Fundamental Research Funds from Central Universities of Central South University.We would like to extend our gratitude to Yinjia Zhang and Liangjin Gong from Ke Du's group at Central South University for their technical support and useful discussions.
文摘Single-crystalline layered oxide materials for lithium-ion batteries are featured by their excellent capacity retention over their polycrystalline counterparts,making them sought-after cathode candidates.Their capacity degradation,however,becomes more severe under high-voltage cycling,hindering many high-energy applications.It has long been speculated that the interplay among composition heterogeneity,lattice deformation,and redox stratification could be a driving force for the performance decay.The underlying mechanism,however,is not well-understood.In this study,we use X-ray microscopy to systematically examine single-crystalline NMC particles at the mesoscale.This technique allows us to capture detailed signals of diffraction,spectroscopy,and fluorescence,offering spatially resolved multimodal insights.Focusing on early high-voltage charging cycles,we uncover heterogeneities in valence states and lattice structures that are inherent rather than caused by electrochemical abuse.These heterogeneities are closely associated with compositional variations within individual particles.Our findings provide useful insights for refining material synthesis and processing for enhanced battery longevity and efficiency.
基金supported partly by BNL LDRD funding (24-067). We thank the user support provided by the CFN staff Kim Kisslinger and Fernando Camino for FIB-SEM training and help with sample preparation. We acknowledge the sample preparation of porous Cu and LiMn2O4 battery electrodes by Qingkun Meng and Cheng-Hung Lin from the Chen-Wiegart group at Stony Brook University and Brookhaven National Laboratory. We also thank the HXN beamline staff, Ajith Pattammattel and Xiaojing Huang, for their support of the XRF tomography measurement setup and data analysis.
文摘A long-standing challenge in tomography is the‘missing wedge’problem,which arises when the acquisition of projection images within a certain angular range is restricted due to geometrical constraints.This incomplete dataset results in significant artifacts and poor resolution in the reconstructed image.To tackle this challenge,we propose an approach dubbed Perception Fused Iterative Tomography Reconstruction Engine,which integrates a convolutional neural network(CNN)with perceptional knowledge as a smart regularizer into an iterative solving engine.We employ the Alternating Direction Method of Multipliers to optimize the solution in both physics and image domains,thereby achieving a physically coherent and visually enhanced result.We demonstrate the effectiveness of the proposed approach using various experimental datasets obtained with different x-ray microscopy techniques.All show significantly improved reconstruction even with a missing wedge of over 100 degrees−a scenario where conventional methods fail.Notably,it also improves the reconstruction in case of sparse projections,despite the network not being specifically trained for that.This demonstrates the robustness and generality of our method of addressing commonly occurring challenges in 3D x-ray imaging applications for real-world problems.
文摘Nanostructured silicon has generated significant excitement for use as the anode material for lithium-ion batteries; however, more effort is needed to produce nanostructured silicon in a scalable fashion and with good performance. Here, we present a direct preparation of porous silicon nanoparticles as a new kind of nanostructured silicon using a novel two-step approach combining controlled boron doping and facile electroless etching. The porous silicon nanoparticles have been successfully used as high performance lithium-ion battery anodes, with capacities around 1,400 mA.h/g achieved at a current rate of 1 A/g, and 1,000 mA.h/g achieved at 2 A/g, and stable operation when combined with reduced graphene oxide and tested over up to 200 cycles. We attribute the overall good performance to the combination of porous silicon that can accommodate large volume change during cycling and provide large surface area accessible to electrolyte, and reduced graphene oxide that can serve as an elastic and electrically conductive matrix for the porous silicon nanoparticles.
文摘Silicon （Si） has the highest known theoretical specific capacity （3,590 mAh/g for Li1.5Si4, and 4,200 mAh/g for Li22Si4） as a lithium-ion battery anode, and has attracted extensive interest in the past few years. However, its application is limited by poor cyclability and early capacity fading due to significant volume changes during lithiation and delithiation processes. In this work, we report a coaxial silicon/anodic titanium oxide/silicon （Si-ATO--Si） nanotube array structure grown on a titanium substrate demonstrating excellent electrochemical cyclability. The ATO nanotube scaffold used for Si deposition has many desirable features, such as a rough surface for enhanced Si adhesion, and direct contact with the Ti substrate working as current collector. More importantly, our ATO scaffold provides a rather unique advantage in that Si can be loaded on both the inner and outer surfaces, and an inner pore can be retained to provide room for Si volume expansion. This coaxial structure shows a capacity above 1,500 mAh/g after 100 cycles, with less than 0.05% decay per cycle. Simulations show that this improved performance can be attributed to the lower stress induced on Si layers upon lithiation/delithiation compared with some other recently reported Si-based nanostructures.
基金supported by the LDRD project 24255 received from Brookhaven National Laboratory.Z.LM.T.are supported by the Department of Energy,Basic Energy Sciences under project DE-SC0019111。
文摘Improving the spatial and spectral resolution of 2D X-ray near-edge absorption structure(XANES)has been a decade-long pursuit to probe local chemical reactions at the nanoscale.However,the poor signal-to-noise ratio in the measured images poses significant challenges in quantitative analysis,especially when the element of interest is at a low concentration.In this work,we developed a postimaging processing method using deep neural network to reliably improve the signal-to-noise ratio in the XANES images.
