Collaborative Research: Data-driven investigation of redox heterogeneity and dynamics in composite electrodes of batteries

Purdue University, West Lafayette IN

Investigators

Abstract

Spatial nonuniformity of charges is a prevalent feature in electrochemical systems – such nonuniformity associated with chemical reactions is termed redox heterogeneity. Subtle variations of the local chemical environment at the atomic scale, such as material defects, can alter thermodynamics and kinetics and create nonuniform charge distribution across a wide range of length scales. Extracting, correlating, and predicting the inter-relationship between the chemical state, charge (re)distribution, and material degradation represents a daunting challenge in electrochemical systems. This project uses a data-driven approach to probe and analyze the structural, chemical, and mechanical heterogeneity at multiple time and length scales in composite electrodes for Li-ion batteries. Such knowledge is crucial to elucidating the aging mechanisms of battery materials, which in turn will contribute to the novel synthesis of materials with enhanced performance and reliability. The collaborative efforts between University of Texas-Austin and Purdue University will provide unique opportunities for recruitment of under-represented students, engagement with the science-technology-entrepreneurship training program, and convergent research through the university–national lab educational collaborations. The project aims to investigate the atomistic fingerprints of the formation and evolution of structural and chemical defects with advanced synchrotron diagnostics and theoretical models. The following research tasks will be conducted. (i) Understand the interplay between redox heterogeneity and structural defects within individual cathode particles through correlative multi-modal imaging, (ii) Determine the particle/cluster-level spatiotemporal statistics of redox heterogeneity and its dynamic response to externally applied electrochemical reaction driving force through in-situ/operando imaging and machine learning-assisted data mining, (iii) Combine in-situ X-ray imaging, data mining, and electro-chemo-mechanics theory to understand the effect of the local charge heterogeneity on the degradation and failure in large-format, industry-relevant battery cells. Overall, the project formulates a data-driven study of the atomistic informatics, morphological features, chemical heterogeneity, reaction dynamics, and electrochemical performance in model electrochemical systems. The use of novel synchrotron analytics, including the in-situ/operando x-ray spectroscopy, nano-focus x-ray diffraction, and 3D micro-/nano- tomography and laminography, offers unprecedented resolution and sensitivity to unravel the kinetics of local chemical coordination and bonding environment. The outcome will draw a conceptually radical spectrum of an essential feature of redox-active materials through well-integrated high-throughput experiments, data mining, and theoretical analysis. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.