Search for: All records

Despite their rapid emergence as the dominant paradigm for electrochemical energy storage, the full promise of lithium-ion batteries is yet to be fully realized, partly because of challenges in adequately resolving common degradation mechanisms. Positive electrodes of Li-ion batteries store ions in interstitial sites based on redox reactions throughout their interior volume. However, variations in the local concentration of inserted Li-ions and inhomogeneous intercalation-induced structural transformations beget substantial stress. Such stress can accumulate and ultimately engender substantial delamination and transgranular/intergranular fracture in typically brittle oxide materials upon continuous electrochemical cycling. This perspective highlights the coupling between electrochemistry, mechanics, and geometry spanning key electrochemical processes: surface reaction, solid-state diffusion, and phase nucleation/transformation in intercalating positive electrodes. In particular, we highlight recent findings on tunable material design parameters that can be used to modulate the kinetics and thermodynamics of intercalation phenomena, spanning the range from atomistic and crystallographic materials design principles (based on alloying, polymorphism, and pre-intercalation) to emergent mesoscale structuring of electrode architectures (through control of crystallite dimensions and geometry, curvature, and external strain). This framework enables intercalation chemistry design principles to be mapped to degradation phenomena based on consideration of mechanics coupling across decades of length scales. Scale-bridging characterization and modeling, along with materials design, holds promise for deciphering mechanistic understanding, modulating multiphysics couplings, and devising actionable strategies to substantially modify intercalation phase diagrams in a manner that unlocks greater useable capacity and enables alleviation of chemo-mechanical degradation mechanisms. 

Abstract Automated particle segmentation and feature analysis of experimental image data are indispensable for data-driven material science. Deep learning-based image segmentation algorithms are promising techniques to achieve this goal but are challenging to use due to the acquisition of a large number of training images. In the present work, synthetic images are applied, resembling the experimental images in terms of geometrical and visual features, to train the state-of-art Mask region-based convolutional neural networks to segment vanadium pentoxide nanowires, a cathode material within optical density-based images acquired using spectromicroscopy. The results demonstrate the instance segmentation power in real optical intensity-based spectromicroscopy images of complex nanowires in overlapped networks and provide reliable statistical information. The model can further be used to segment nanowires in scanning electron microscopy images, which are fundamentally different from the training dataset known to the model. The proposed methodology can be extended to any optical intensity-based images of variable particle morphology, material class, and beyond.