Accurately capturing the architecture of single lithium-ion electrode particles is necessary for understanding their performance limitations and degradation mechanisms through multi-physics modeling.Information is dra...Accurately capturing the architecture of single lithium-ion electrode particles is necessary for understanding their performance limitations and degradation mechanisms through multi-physics modeling.Information is drawn from multimodal microscopy techniques to artificially generate LiNi_(0.5)Mn_(0.3)Co_(0.2)O_(2) particles with full sub-particle grain detail.Statistical representations of particle architectures are derived from X-ray nano-computed tomography data supporting an‘outer shell’model,and sub-particle grain representations are derived from focused-ion beam electron backscatter diffraction data supporting a‘grain’model.A random field model used to characterize and generate the outer shells,and a random tessellation model used to characterize and generate grain architectures,are combined to form a multi-scale model for the generation of virtual electrode particles with fullgrain detail.This work demonstrates the possibility of generating representative single electrode particle architectures for modeling and characterization that can guide synthesis approaches of particle architectures with enhanced performance.展开更多
基金This work was authored in part by the National Renewable Energy Laboratory,operated by Alliance for Sustainable Energy,LLC,for the U.S.Department of Energy(DOE)under Contract No.DE-AC36-08GO28308Funding was provided by the U.S.DOE Office of Vehicle Technology Extreme Fast Charge Program,program manager Samuel Gillard.The views expressed in the article do not necessarily represent the views of the DOE or the U.S.Government.The U.S.Government retains and the publisher,by accepting the article for publication,acknowledges that the U.S.Government retains a nonexclusive,paid-up,irrevocable,worldwide license to publish or reproduce the published form of this work,or allow others to do so,for U.S.Government purposes.
文摘Accurately capturing the architecture of single lithium-ion electrode particles is necessary for understanding their performance limitations and degradation mechanisms through multi-physics modeling.Information is drawn from multimodal microscopy techniques to artificially generate LiNi_(0.5)Mn_(0.3)Co_(0.2)O_(2) particles with full sub-particle grain detail.Statistical representations of particle architectures are derived from X-ray nano-computed tomography data supporting an‘outer shell’model,and sub-particle grain representations are derived from focused-ion beam electron backscatter diffraction data supporting a‘grain’model.A random field model used to characterize and generate the outer shells,and a random tessellation model used to characterize and generate grain architectures,are combined to form a multi-scale model for the generation of virtual electrode particles with fullgrain detail.This work demonstrates the possibility of generating representative single electrode particle architectures for modeling and characterization that can guide synthesis approaches of particle architectures with enhanced performance.
