Beyond Constant Current: Origin of Pulse-Induced Activation in Phase-Transforming Battery Electrodes.

Mechanistic understanding of phase transformation dynamics during battery charging and discharging is crucial toward rationally improving intercalation electrodes. Most studies focus on constant-current conditions. However, in real battery operation, such as in electric vehicles during discharge, the current is rarely constant. In this work we study current pulsing in LiXFePO4 (LFP), a model and technologically important phase-transforming electrode. A current-pulse activation effect has been observed in LFP, which decreases the overpotential by up to ∼70% after a short, high-rate pulse. This effect persists for hours or even days. Using scanning transmission X-ray microscopy and operando X-ray diffraction, we link this long-lived activation effect to a pulse-induced electrode homogenization on both the intra- and interparticle length scales, i.e., within and between particles. Many-particle phase-field simulations explain how such pulse-induced homogeneity contributes to the decreased electrode overpotential. Specifically, we correlate the extent and duration of this activation to lithium surface diffusivity and the magnitude of the current pulse. This work directly links the transient electrode-level electrochemistry to the underlying phase transformation and explains the critical effect of current pulses on phase separation, with significant implication on both battery round-trip efficiency and cycle life. More broadly, the mechanisms revealed here likely extend to other phase-separating electrodes, such as graphite.

Correlative image learning of chemo-mechanics in phase-transforming solids.

Constitutive laws underlie most physical processes in nature. However, learning such equations in heterogeneous solids (for example, due to phase separation) is challenging. One such relationship is between composition and eigenstrain, which governs the chemo-mechanical expansion in solids. Here we developed a generalizable, physically constrained image-learning framework to algorithmically learn the chemo-mechanical constitutive law at the nanoscale from correlative four-dimensional scanning transmission electron microscopy and X-ray spectro-ptychography images. We demonstrated this approach on LiXFePO4, a technologically relevant battery positive electrode material. We uncovered the functional form of the composition-eigenstrain relation in this two-phase binary solid across the entire composition range (0 ≤ X ≤ 1), including inside the thermodynamically unstable miscibility gap. The learned relation directly validates Vegard's law of linear response at the nanoscale. Our physics-constrained data-driven approach directly visualizes the residual strain field (by removing the compositional and coherency strain), which is otherwise impossible to quantify. Heterogeneities in the residual strain arise from misfit dislocations and were independently verified by X-ray diffraction line profile analysis. Our work provides the means to simultaneously quantify chemical expansion, coherency strain and dislocations in battery electrodes, which has implications on rate capabilities and lifetime. Broadly, this work also highlights the potential of integrating correlative microscopy and image learning for extracting material properties and physics.

In vivo selection of highly metastatic human ovarian cancer sublines reveals role for AMIGO2 in intra-peritoneal metastatic regulation.

The majority of women with ovarian cancer are diagnosed with metastatic disease, therefore elucidating molecular events that contribute to successful metastatic dissemination may identify additional targets for therapeutic intervention and thereby positively impact survival. Using two human high grade serous ovarian cancer cell lines with inactive TP53 and multiple rounds of serial in vivo passaging, we generated sublines with significantly accelerated intra-peritoneal (IP) growth. Comparative analysis of the parental and IP sublines identified a common panel of differentially expressed genes. The most highly differentially expressed gene, upregulated by 60-65-fold in IP-selected sublines, was the type I transmembrane protein AMIGO2. As the role of AMIGO2 in ovarian cancer metastasis remains unexplored, CRISPR/Cas9 was used to reduce AMIGO2 expression, followed by in vitro and in vivo functional analyses. Knockdown of AMIGO2 modified the sphere-forming potential of ovarian cancer cells, reduced adhesion and invasion in vitro, and significantly attenuated IP metastasis. These data highlight AMIGO2 as a new target for a novel anti-metastatic therapeutic approach aimed at blocking cohesion, survival, and adhesion of metastatic tumorspheres.

Closed-loop optimization of fast-charging protocols for batteries with machine learning.

Simultaneously optimizing many design parameters in time-consuming experiments causes bottlenecks in a broad range of scientific and engineering disciplines1,2. One such example is process and control optimization for lithium-ion batteries during materials selection, cell manufacturing and operation. A typical objective is to maximize battery lifetime; however, conducting even a single experiment to evaluate lifetime can take months to years3-5. Furthermore, both large parameter spaces and high sampling variability3,6,7 necessitate a large number of experiments. Hence, the key challenge is to reduce both the number and the duration of the experiments required. Here we develop and demonstrate a machine learning methodology  to efficiently optimize a parameter space specifying the current and voltage profiles of six-step, ten-minute fast-charging protocols for maximizing battery cycle life, which can alleviate range anxiety for electric-vehicle users8,9. We combine two key elements to reduce the optimization cost: an early-prediction model5, which reduces the time per experiment by predicting the final cycle life using data from the first few cycles, and a Bayesian optimization algorithm10,11, which reduces the number of experiments by balancing exploration and exploitation to efficiently probe the parameter space of charging protocols. Using this methodology, we rapidly identify high-cycle-life charging protocols among 224 candidates in 16 days (compared with over 500 days using exhaustive search without early prediction), and subsequently validate the accuracy and efficiency of our optimization approach. Our closed-loop methodology automatically incorporates feedback from past experiments to inform future decisions and can be generalized to other applications in battery design and, more broadly, other scientific domains that involve time-intensive experiments and multi-dimensional design spaces.

Evolution of the Solid-Electrolyte Interphase on Carbonaceous Anodes Visualized by Atomic-Resolution Cryogenic Electron Microscopy.

The stability of modern lithium-ion batteries depends critically on an effective solid-electrolyte interphase (SEI), a passivation layer that forms on the carbonaceous negative electrode as a result of electrolyte reduction. However, a nanoscopic understanding of how the SEI evolves with battery aging remains limited due to the difficulty in characterizing the structural and chemical properties of this sensitive interphase. In this work, we image the SEI on carbon black negative electrodes using cryogenic transmission electron microscopy (cryo-TEM) and track its evolution during cycling. We find that a thin, primarily amorphous SEI nucleates on the first cycle, which further evolves into one of two distinct SEI morphologies upon further cycling: (1) a compact SEI, with a high concentration of inorganic components that effectively passivates the negative electrode; and (2) an extended SEI spanning hundreds of nanometers. This extended SEI grows on particles that lack a compact SEI and consists primarily of alkyl carbonates. The diversity in observed SEI morphologies suggests that SEI growth is a highly heterogeneous process. The simultaneous emergence of these distinct SEI morphologies highlights the necessity of effective passivation by the SEI, as large-scale extended SEI growths negatively impact lithium-ion transport, contribute to capacity loss, and may accelerate battery failure.

Fluid-enhanced surface diffusion controls intraparticle phase transformations.

Phase transformations driven by compositional change require mass flux across a phase boundary. In some anisotropic solids, however, the phase boundary moves along a non-conductive crystallographic direction. One such material is LiXFePO4, an electrode for lithium-ion batteries. With poor bulk ionic transport along the direction of phase separation, it is unclear how lithium migrates during phase transformations. Here, we show that lithium migrates along the solid/liquid interface without leaving the particle, whereby charge carriers do not cross the double layer. X-ray diffraction and microscopy experiments as well as ab initio molecular dynamics simulations show that organic solvent and water molecules promote this surface ion diffusion, effectively rendering LiXFePO4 a three-dimensional lithium-ion conductor. Phase-field simulations capture the effects of surface diffusion on phase transformation. Lowering surface diffusivity is crucial towards supressing phase separation. This work establishes fluid-enhanced surface diffusion as a key dial for tuning phase transformation in anisotropic solids.

Origin and hysteresis of lithium compositional spatiodynamics within battery primary particles.

The kinetics and uniformity of ion insertion reactions at the solid-liquid interface govern the rate capability and lifetime, respectively, of electrochemical devices such as Li-ion batteries. Using an operando x-ray microscopy platform that maps the dynamics of the Li composition and insertion rate in Li(x)FePO4, we found that nanoscale spatial variations in rate and in composition control the lithiation pathway at the subparticle length scale. Specifically, spatial variations in the insertion rate constant lead to the formation of nonuniform domains, and the composition dependence of the rate constant amplifies nonuniformities during delithiation but suppresses them during lithiation, and moreover stabilizes the solid solution during lithiation. This coupling of lithium composition and surface reaction rates controls the kinetics and uniformity during electrochemical ion insertion.