Understanding the Electrochemical Performance of FeS2 Conversion Cathodes.

Conversion cathodes represent a viable route to improve rechargeable Li+ battery energy densities, but their poor electrochemical stability and power density have impeded their practical implementation. Here, we explore the impact cell fabrication, electrolyte interaction, and current density have on the electrochemical performance of FeS2/Li cells by deconvoluting the contributions of the various conversion and intercalation reactions to the overall capacity. By varying the slurry composition and applied pressure, we determine that the capacity loss is primarily due to the large volume changes during (de)lithiation, leading to a degradation of the conductive matrix. Through the application of an external pressure, the loss is minimized by maintaining the conductive matrix. We further determine that polysulfide loss can be minimized by increasing the current density (>C/10), thus reducing the sulfur formation period. Analysis of the kinetics determines that the conversion reactions are rate-limiting, specifically the formation of metallic iron at rates above C/8. While focused on FeS2, our findings on the influence of pressure, electrolyte interaction, and kinetics are broadly applicable to other conversion cathode systems.

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.

Cryogenic electron microscopy reveals that applied pressure promotes short circuits in Li batteries.

Li metal anodes are enticing for batteries due to high theoretical charge storage capacity, but commercialization is plagued by dendritic Li growth and short circuits when cycled at high currents. Applied pressure has been suggested to improve morphology, and therefore performance. We hypothesized that increasing pressure would suppress dendritic growth at high currents. To test this hypothesis, here, we extensively use cryogenic scanning electron microscopy to show that varying the applied pressure from 0.01 to 1 MPa has little impact on Li morphology after one deposition. We show that pressure improves Li density and preserves Li inventory after 50 cycles. However, contrary to our hypothesis, pressure exacerbates dendritic growth through the separator, promoting short circuits. Therefore, we suspect Li inventory is better preserved in cells cycled at high pressure only because the shorts carry a larger portion of the current, with less being carried by electrochemical reactions that slowly consume Li inventory.

Quantifying the unknown impact of segmentation uncertainty on image-based simulations.

Image-based simulation, the use of 3D images to calculate physical quantities, relies on image segmentation for geometry creation. However, this process introduces image segmentation uncertainty because different segmentation tools (both manual and machine-learning-based) will each produce a unique and valid segmentation. First, we demonstrate that these variations propagate into the physics simulations, compromising the resulting physics quantities. Second, we propose a general framework for rapidly quantifying segmentation uncertainty. Through the creation and sampling of segmentation uncertainty probability maps, we systematically and objectively create uncertainty distributions of the physics quantities. We show that physics quantity uncertainty distributions can follow a Normal distribution, but, in more complicated physics simulations, the resulting uncertainty distribution can be surprisingly nontrivial. We establish that bounding segmentation uncertainty can fail in these nontrivial situations. While our work does not eliminate segmentation uncertainty, it improves simulation credibility by making visible the previously unrecognized segmentation uncertainty plaguing image-based simulation.

Effects of Applied Interfacial Pressure on Li-Metal Cycling Performance and Morphology in 4 M LiFSI in DME.

Lithium-metal anodes can theoretically enable 10× higher gravimetric capacity than conventional graphite anodes. However, Li-metal anode cycling has proven difficult due to porous and dendritic morphologies, extensive parasitic solid electrolyte interphase reactions, and formation of dead Li. We systematically investigate the effects of applied interfacial pressure on Li-metal anode cycling performance and morphology in the recently developed and highly efficient 4 M lithium bis(fluorosulfonyl)imide in 1,2-dimethoxyethane electrolyte. We present cycling, morphology, and impedance data at a current density of 0.5 mA/cm2 and a capacity of 2 mAh/cm2 at applied interfacial pressures of 0, 0.01, 0.1, 1, and 10 MPa. Cryo-focused ion beam milling and cryo-scanning electron microscopy imaging in cross section reveal that increasing the applied pressure during Li deposition from 0 to 10 MPa leads to greater than a fivefold reduction in thickness (and therefore volume) of the deposited Li. This suggests that pressure during cycling can have a profound impact on the practical volumetric energy density for Li-metal anodes. A "goldilocks zone" of cell performance is observed at intermediate pressures of 0.1-1 MPa. Increasing pressure from 0 to 1 MPa generally improves cell-to-cell reproducibility, cycling stability, and Coulombic efficiency. However, the highest pressure (10 MPa) results in high cell overpotential and evidence of soft short circuits, which likely result from transport limitations associated with increased pressure causing local pore closure in the separator. All cells exhibit at least some signs of cycling instability after 50 cycles when cycled to 2 mAh/cm2 with thin 50 µm Li counter electrodes, though instability decreases with increasing pressure. In contrast, cells cycled to only 1 mAh/cm2 perform well for 50 cycles, indicating that capacity plays an important role in cycling stability.

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.

Controlling Binder Adhesion to Impact Electrode Mesostructures and Transport.

The complex three-phase composition of lithium-ion battery electrodes, containing an ion-conducting pore phase, a nanoporous electron-conducting carbon binder domain (CBD) phase, and an active material (AM) phase, provides several avenues of mesostructural engineering to enhance battery performance. We demonstrate a promising strategy for engineering electrode mesostructures by controlling the strength of adhesion between the AM and CBD phases. Using high-fidelity, physics-based colloidal and granular dynamics simulations, we predict that this strategy can provide significant control over electrochemical transport-relevant properties such as ionic conductivity, electronic conductivity, and available AM-electrolyte interface area. Importantly, the proposed strategy could be experimentally realized through surface functionalization of the AM and CBD phases and would be compatible with traditional electrode manufacturing methods.