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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. 

Light-weighting vehicular components through adoption of light-metal structural alloys holds promise for reducing the fuel consumption of internal combustion engine vehicles and increasing the range of battery electric vehicles. However, the alloyed microstructure and surface precipitates of aluminum alloys render these materials susceptible to corrosion under modest excursions from neutral pH. Traditional chromium-based anodic passivation layers are subject to increasingly stringent environmental regulations, whereas options for sacrificial cathodic films are sparse for electropositive metals. While hybrid nanocomposite coatings have shown initial promise, mechanistic underpinnings remain poorly understood. Here, a fully imidized polyetherimide (PEI) resin is utilized as the continuous phase with inclusion of unfunctionalized exfoliated graphite (UFG). A comprehensive investigation of the mechanisms of corrosion protection reveals key fundamental design principles underpinning corrosion inhibition. First, strong interfacial adhesion, which for PEI is facilitated by binding of imide carbonyl moieties to Lewis acidic sites on Al surfaces. Second, the miscibility of ion-impervious nanoscopic UFG fillers and stabilization of a substantial interphase region at UFG/PEI boundaries that result in minimizing the free volume at the filler/polymer interface. Finally, extended tortuosity of ion diffusion pathways imbued by the below-percolation-threshold 2D fillers. These three design principles help govern and modulate ion transport from electrolyte/coating interfaces to the coating/metal interface and are crucial for the extended preservation of barrier properties. The results suggest an approach to systematically activate multiple modes of corrosion inhibition through rational design of hybrid nanocomposite coatings across hard-to-abate sectors where light metal alloys are likely to play an increasingly prominent role.

 

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.