CeO 2 (Alfa Aesar 11328) was ball-milled with 16 wt% starch (Mallinckrodt 8188-2) for 19 to 24 h in anhydrous ethanol, and, after drying, mixed with 42 wt% graphite powders (-100 mesh, Alfa Aesar 14735). The composite powder was uniaxially pressed into quarter-circular-arc pieces. Subsequent heat-treatment at 1,500° C for 5 hr induced both thermal decomposition of the starch and graphite poreformers and sintering to yield suitably mechanically robust porous monoliths. The pieces measured approximately 40 mm in arc length, 8 mm in thickness, 9 mm in height. Porosity was determined by a simple measurement of the geometric dimensions and sample mass and comparing to the theoretical density of CeO 2 , 7.2 g cm-3 ; it was found to be 80 %. X-ray powder diffraction data were collected using a Philip X'Pert PRO diffractometer (Cu kα, 45 kV, 40mA). Scanning electron microscopy was performed on a Carl-Zeiss 1550 VP.

Accurately predicting lifetime of complex systems like lithium-ion batteries is crucial for accelerating technology development. However, diverse aging 1 mechanisms, significant device variability, and varied operating conditions have remained major challenges. To study this problem, we generated a dataset consisting of 124 commercial lithium-iron-phosphate/graphite cells cycled under fast charging conditions. The cells exhibited widely varied cycle lives spanning from 150 to 2,300 cycles, with end-of-life defined as 20% degradation from nominal capacity. Using discharge voltage curves from early cycles yet to exhibit capacity degradation, we apply machine learning tools to predict cycle life with less than 15% error on average, which is improved to ~8% error by incorporating additional data. Our work represents a significant improvement over previous predictions that generally required data corresponding to >5% capacity degradation, without needing specialized diagnostics. Additionally, it highlights the promise of combining data generation with data-driven modeling to predict the behavior of complex and variable systems. Main Lithium-ion batteries are deployed in a wide range of applications due to their low and falling costs, high energy densities, and long cycle lives. 1-3 However, as is the case with many chemical, mechanical, and electronics systems, long battery cycle life implies delayed feedback of performance during development and manufacture, often many months to years. Accurately predicting cycle life using early-cycle data would accelerate this feedback loop as well as enable estimation of battery life expectancy for use in consumer electronics, electric vehicles, and second-life applications. 4-6

Lithium-rich layered transition metal oxide positive electrodes offer access to anion redox at high potentials, thereby promising high energy densities for lithium-ion batteries. However, anion redox is also associated with several unfavorable electrochemical properties, such as open-circuit voltage hysteresis. Here we reveal that in Li1.17–xNi0.21Co0.08Mn0.54O2, these properties arise from a strong coupling between anion redox and cation migration. We combine various X-ray spectroscopic, microscopic, and structural probes to show that partially reversible transition metal migration decreases the potential of the bulk oxygen redox couple by > 1 V, leading to a reordering in the anionic and cationic redox potentials during cycling. First principles calculations show that this is due to the drastic change in the local oxygen coordination environments associated with the transition metal migration. We propose that this mechanism is involved in stabilizing the oxygen redox couple, which we observe spectroscopically to persist for 500 charge/discharge cycles.

Abstract: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.We develop an operando X-ray microscopy platform that maps the dynamics of the Li composition and insertion rate in LiXFePO4, and show that nanoscale spatial variations in rate and in composition control the lithiation pathway at the sub-particle 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.One Sentence Summary: X-ray microscopy reveals the nanoscale evolution of composition and reaction rate inside a Li-ion battery during cycling Main Text: The insertion of a guest ion into the host crystal is the fundamental reaction underpinning insertion electrochemistry and has been applied to store energy (1), tune catalysts (2), and switch optoelectronic properties (3). In Li-ion batteries, for example, Li ions from the 2 liquid electrolyte insert into solid host particles in the electrode. Nanoscale intraparticle electrochemical inhomogeneities in phase and in composition are responsible for mechanical strain and fracture which decrease the reversibility of the reaction (4). Moreover, these nonuniformities make it difficult to correlate current-voltage measurements to microscopic ion insertion mechanisms. Simultaneously quantifying nonuniform nanoscale reaction kinetics and the underlying material composition at the solid-liquid interface holds the key to improving device performance.A gold standard material for investigating ion insertion reactions is LiXFePO4 (0