Controlling Binder Adhesion to Impact Electrode Mesostructures and Transport

Probing the Role of Multi-scale Heterogeneity in Graphite Electrodes for Extreme Fast Charging

Electrode-scale heterogeneity can combine with complex electrochemical interactions to impede lithium-ion battery performance, particularly during fast charging. This study investigates the influence of electrode heterogeneity at different scales on the lithium-ion battery electrochemical performance under operational extremes. We employ image-based mesoscale simulation in conjunction with a three-dimensional electrochemical model to predict performance variability in 14 graphite electrode X-ray computed tomography data sets. Our analysis reveals that the tortuous anisotropy stemming from the variable particle morphology has a dominating influence on the overall cell performance. Cells with platelet morphology achieve lower capacity, higher heat generation rates, and severe plating under extreme fast charge conditions. On the contrary, the heterogeneity due to the active material clustering alone has minimal impact. Our work suggests that manufacturing electrodes with more homogeneous and isotropic particle morphology will improve electrochemical performance and improve safety, enabling electromobility.

Probing the Influence of Multiscale Heterogeneity on Effective Properties of Graphite Electrodes

Graphite electrodes in the lithium-ion battery exhibit various particle shapes, including spherical and platelet morphologies, which influence structural and electrochemical characteristics. It is well established that porous structures exhibit spatial heterogeneity, and the particle morphology can influence transport properties. The impact of the particle morphology on the heterogeneity and anisotropy of geometric and transport properties has not been previously studied. This study characterizes the spatial heterogeneities of 18 graphite electrodes at multiple length scales by calculating and comparing the structural anisotropy, geometric quantities, and transport properties (pore-scale tortuosity and electrical conductivity). We found that the particle morphology and structural anisotropy play an integral role in determining the spatial heterogeneity of directional tortuosity and its dependency on pore-scale heterogeneity. Our analysis reveals that the magnitude of in-plane and through-plane tortuosity difference influences the multiscale heterogeneity in graphite electrodes.

Deep learning-based segmentation of lithium-ion battery microstructures enhanced by artificially generated electrodes

Accurate 3D representations of lithium-ion battery electrodes, in which the active particles, binder and pore phases are distinguished and labeled, can assist in understanding and ultimately improving battery performance. Here, we demonstrate a methodology for using deep-learning tools to achieve reliable segmentations of volumetric images of electrodes on which standard segmentation approaches fail due to insufficient contrast. We implement the 3D U-Net architecture for segmentation, and, to overcome the limitations of training data obtained experimentally through imaging, we show how synthetic learning data, consisting of realistic artificial electrode structures and their tomographic reconstructions, can be generated and used to enhance network performance. We apply our method to segment x-ray tomographic microscopy images of graphite-silicon composite electrodes and show it is accurate across standard metrics. We then apply it to obtain a statistically meaningful analysis of the microstructural evolution of the carbon-black and binder domain during battery operation.

How Machine Learning Will Revolutionize Electrochemical Sciences

Electrochemical systems function via interconversion of electric charge and chemical species and represent promising technologies for our cleaner, more sustainable future. However, their development time is fundamentally limited by our ability to identify new materials and understand their electrochemical response. To shorten this time frame, we need to switch from the trial-and-error approach of finding useful materials to a more selective process by leveraging model predictions. Machine learning (ML) offers data-driven predictions and can be helpful. Herein we ask if ML can revolutionize the development cycle from decades to a few years. We outline the necessary characteristics of such ML implementations. Instead of enumerating various ML algorithms, we discuss scientific questions about the electrochemical systems to which ML can contribute.

Variation in the interface strength of silicon with surface engineered Ti3C2 MXenes

Current advancements in battery technologies require electrodes to combine high-performance active materials such as Silicon (Si) with two-dimensional materials such as transition metal carbides (MXenes) for prolonged cycle stability and enhanced electrochemical performance. More so, it is the interface between these materials, which is the nexus for their applicatory success. Herein, the interface strength variations between amorphous Si and Ti3C2Tx MXenes are determined as the MXene surface functional groups (Tx) are changed using first principles calculations. Si is interfaced with three Ti3C2 MXene substrates having surface -OH, -OH and -O mixed, and -F functional groups. Density functional theory (DFT) results reveal that completely hydroxylated Ti3C2 has the highest interface strength of 0.6 J m-2 with amorphous Si. This interface strength value drops as the proportion of surface -O and -F groups increases. Additional analysis of electron redistribution and charge separation across the interface is provided for a complete understanding of underlying physico-chemical factors affecting the surface chemistry and resultant interface strength values. The presented comprehensive analysis of the interface aims to develop sophisticated MXene based electrodes by their targeted surface engineering.

Understanding the Strength of the Selenium-Graphene Interfaces for Energy Storage Systems

We present comprehensive first-principles density functional theory (DFT) analyses of the interfacial strength and bonding mechanisms between crystalline and amorphous selenium (Se) with graphene (Gr), a promising duo for energy storage applications. Comparative interface analyses are presented on amorphous silicon (Si) with graphene and crystalline Se with a conventional aluminum (Al) current collector. The interface strengths of monoclinic Se (0.43 J m^-2) and amorphous Si with graphene (0.41 J m^-2) are similar in magnitude. While both materials (c-Se, a-Si) are bonded loosely by van der Waals (vdW) forces over graphene, interfacial electron exchange is higher for a-Si/graphene. This is further elaborated by comparing the potential energy step and charge transfer (Δq) across the graphene interfaces. The interface strength of c-Se on a 3D Al current collector is higher (0.99 J m^-2), suggesting a stronger adhesion. Amorphous Se with graphene has comparable interface strength (0.34 J m^-2), but electron exchange in this system is slightly distinct from monoclinic Se. The electronic characteristics and bonding mechanisms are different for monoclinic and amorphous Se with graphene as they activate graphene via surface charge doping divergently. The implications of these interfacial physicochemical attributes on electrode performance have been discussed. Our findings highlight the complex electrochemical phenomena in Se interfaced with graphene, which may profoundly differ from their "free" counterparts.