3D Detection of Lithiation and Lithium Plating in Graphite Anodes during Fast Charging

3D Pathways Enabling Highly-Efficient Lithium Reservoir for Fast-Charging Batteries

Enhancing the mobility of lithium-ions (Li+) through surface engineering is one of major challenges facing fast-charging lithium-ion batteries (LIBs). In case of demanding charging conditions, the use of a conventional artificial graphite (AG) anode leads to an increase in operating temperature and the formation of lithium dendrites on the anode surface. In this study, a biphasic zeolitic imidazolate framework (ZIF)-AG anode, designed strategically and coated with a mesoporous material, is verified to improve the pathways of Li+ and electrons under a high charging current density. In particular, the graphite surface is treated with a coating of a ZIF-8-derived carbon nanoparticles, which addresses sufficient surface porosity, enabling this material to serve as an electrolyte reservoir and facilitate Li+ intercalation. Moreover, the augmentation in specific surface area proves advantageous in reducing the overpotential for interfacial charge transfer reactions. In practical terms, employing a full-cell with the biphasic ZIF-AG anode results in a shorter charging time and improved cycling performance, demonstrating no evidence of Li plating during 300 cycles under 3.0 C-charging and 1.0 C-discharging. The research endeavors to contribute to the progress of anode materials by enhancing their charging capability, aligning with the increasing requirements of the electric vehicle applications.

Surface Decoration of TiC Nanocrystals onto the Graphite Anode Enables Fast-Charging Lithium-Ion Batteries

To significantly reduce the charging time of commercial lithium-ion batteries (LIBs), it is essential to control the surface properties of graphite anodes because the charging process involves sluggish interfacial kinetics between graphite and the electrolyte. For the effective surface modification of graphite, herein we demonstrate the surface decoration with titanium carbide (TiC) nanocrystals onto graphite particles via a simple wet-coating process. The high electrical conductivity, low Li+ adsorption energy, and small surface diffusion barrier of the TiC nanocrystals facilitate fast Li+ adsorption and migration in the graphite surface by reducing the overpotential upon the charging process. The feasibility of the TiC nanocrystal-decorated graphite (TiC@AG) anode is thoroughly examined with an in-depth understanding of the interfacial reaction mechanism. Furthermore, the full-cell with a commercial cathode (LiNi0.8Co0.1Mn0.1O2) and TiC@AG anode demonstrates an impressive capacity retention (94.5%) after 300 cycles under fast-charging condition (3 C-charging and 1 C-discharging) without any sign of Li plating. The charging time of the TiC@AG full-cell was estimated at 16.2 min (80% of state of charge), which is substantially shorter than that of the artificial graphite full-cell. Our findings offer practical insights into the design principles of advanced graphite anodes, contributing to the realization of fast-charging LIBs.

Unraveling the Fundamental Mechanism of Interface Conductive Network Influence on the Fast-Charging Performance of SiO-Based Anode for Lithium-Ion Batteries

HIGHLIGHTS

Influence of interface conductive network on ionic transport and mechanical stability under fast charging is explored for the first time. The mitigation of interface polarization is precisely revealed by the combination of 2D modeling simulation and Cryo-TEM observation, which can be attributed to a higher fraction formation of conductive inorganic species in bilayer SEI, and primarily contributes to a linear decrease in ionic diffusion energy barrier. The improved stress dissipation presented by AFM and Raman shift is critical for the linear reduction in electrode residual stress and thickness swelling. Progress in the fast charging of high-capacity silicon monoxide (SiO)-based anode is currently hindered by insufficient conductivity and notable volume expansion. The construction of an interface conductive network effectively addresses the aforementioned problems; however, the impact of its quality on lithium-ion transfer and structure durability is yet to be explored. Herein, the influence of an interface conductive network on ionic transport and mechanical stability under fast charging is explored for the first time. 2D modeling simulation and Cryo-transmission electron microscopy precisely reveal the mitigation of interface polarization owing to a higher fraction of conductive inorganic species formation in bilayer solid electrolyte interphase is mainly responsible for a linear decrease in ionic diffusion energy barrier. Furthermore, atomic force microscopy and Raman shift exhibit substantial stress dissipation generated by a complete conductive network, which is critical to the linear reduction of electrode residual stress. This study provides insights into the rational design of optimized interface SiO-based anodes with reinforced fast-charging performance.

Large Local Currents in a Lithium-Ion Battery during Rest after Fast Charging

Increasing electric vehicle (EV) adoption requires lithium-ion batteries that can be charged quickly and safely. Some EV batteries have caught on fire despite being neither charged nor discharged. While the lithium that plates on graphite during fast charging affects battery safety, so do the internal ionic currents that can occur when the battery is at rest after charging. These currents are difficult to quantify; the external current that can readily be measured is zero. Here we study a graphite electrode at rest after 6C fast charging using operando X-ray microtomography. We quantify spatially resolved current density distributions that originate at plated lithium and end in underlithiated graphite particles. The average current densities decrease from 1.5 to 0.5 mA cm-2 in about 20 min after charging is stopped. Surprisingly, the range of the stripping current density is independent of time, with outliers above 20 mA cm-2. The persistence of outliers provides a clue as to the origin of catastrophic failure in batteries at rest.

Kinetic Limits of Graphite Anode for Fast-Charging Lithium-Ion Batteries

Fast-charging lithium-ion batteries are highly required, especially in reducing the mileage anxiety of the widespread electric vehicles. One of the biggest bottlenecks lies in the sluggish kinetics of the Li+ intercalation into the graphite anode; slow intercalation will lead to lithium metal plating, severe side reactions, and safety concerns. The premise to solve these problems is to fully understand the reaction pathways and rate-determining steps of graphite during fast Li+ intercalation. Herein, we compare the Li+ diffusion through the graphite particle, interface, and electrode, uncover the structure of the lithiated graphite at high current densities, and correlate them with the reaction kinetics and electrochemical performances. It is found that the rate-determining steps are highly dependent on the particle size, interphase property, and electrode configuration. Insufficient Li+ diffusion leads to high polarization, incomplete intercalation, and the coexistence of several staging structures. Interfacial Li+ diffusion and electrode transportation are the main rate-determining steps if the particle size is less than 10 μm. The former is highly dependent on the electrolyte chemistry and can be enhanced by constructing a fluorinated interphase. Our findings enrich the understanding of the graphite structural evolution during rapid Li+ intercalation, decipher the bottleneck for the sluggish reaction kinetics, and provide strategic guidelines to boost the fast-charging performance of graphite anode.

Pattern Investigation and Quantitative Analysis of Lithium Plating under Subzero Operation of Lithium-Ion Batteries

Safety hazards arising from lithium (Li) plating during the operation of lithium-ion batteries (LIBs) are a constant concern. Herein, this work explores the coaction of low temperatures and current rates (C rates) on Li plating in LIBs by electrochemical tests, material characterization, and numerical analysis. With a decrease in temperature and an increase in C rate, the battery charging process shifts from normal intercalation to Li plating and even ultimately fails at -20 °C and 0.5C. The morphology observations reveal the detailed growth process of individual plated Li through sand-like Li, whisker Li, dendritic Li, mossy Li, and finally bulk Li, as well as aggregated Li from sparse to dense. Through quantitative analysis, the dynamic pattern under long-term cycles is revealed. The low temperature and high C rate will lead to an increase in Li plating capacity and irreversibility, which are further deteriorated with the cycles. In addition, a critical condition of high Li plating and high reversibility at -10 °C and 0.2C is found, and further studies are needed to reveal the competition between kinetics and thermodynamics in the Li plating process. This work provides detailed information on the range and growth process of Li plating and quantifies Li plating, which can be used for practical Li plating prediction and regulation.

Lithium Metal Battery Quality Control via Transformer-CNN Segmentation

Lithium metal battery (LMB) has the potential to be the next-generation battery system because of its high theoretical energy density. However, defects known as dendrites are formed by heterogeneous lithium (Li) plating, which hinders the development and utilization of LMBs. Non-destructive techniques to observe the dendrite morphology often use X-ray computed tomography (XCT) to provide cross-sectional views. To retrieve three-dimensional structures inside a battery, image segmentation becomes essential to quantitatively analyze XCT images. This work proposes a new semantic segmentation approach using a transformer-based neural network called TransforCNN that is capable of segmenting out dendrites from XCT data. In addition, we compare the performance of the proposed TransforCNN with three other algorithms, U-Net, Y-Net, and E-Net, consisting of an ensemble network model for XCT analysis. Our results show the advantages of using TransforCNN when evaluating over-segmentation metrics, such as mean intersection over union (mIoU) and mean Dice similarity coefficient (mDSC), as well as through several qualitatively comparative visualizations.

Hybrid Machine Learning-Enabled Potential Energy Model for Atomistic Simulation of Lithium Intercalation into Graphite from Plating to Overlithiation

Graphite is one of the most widely used negative electrode materials for lithium ion batteries (LIBs). However, because of the rapid growth of demands pursuing higher energy density and charging rates, comprehensive insights into the lithium intercalation and plating processes are critical for further boosting the potential of graphite electrodes. Herein, by utilizing the dihedral-angle-corrected registry-dependent potential (DRIP) (Wen et al., Phys. Rev. B 2018, 98, 235404), the Ziegler-Biersack-Littmark (ZBL) potential (Ziegler and Biersack, Astrophysics, Chemistry, and Condensed Matter; 1985, pp 93-129), and the machine learning-based spectral neighbor analysis (SNAP) potential (Thompson et al., J. Comput, Phys. 2015, 285, 316-330), we have successfully trained a hybrid machine learning-enabled potential energy model capable of simulating a wide spectrum of lithium intercalation scenario from plating to overlithiation. Our extensive atomistic simulations reveal the trapping of intercalated lithium atoms close to the graphite edges due to high hopping barriers, resulting in lithium plating. Furthermore, we report a stable dense graphite intercalation compound (GIC) LiC₄ with a theoretical capacity of 558 mAh/g, wherein lithium atoms occupy alternating upper/lower graphene hollow sites with a nearest Li-Li distance of 2.8 Å. Surprisingly, following the same lithium insertion manner would allow the nearest Li-Li distance to be retained until the capacity reaches 845.2 mAh/g, corresponding to a GIC of LiC_2.6. Hence, the present study demonstrates that the hybrid machine learning approach could further extend the scope of machine learning energy models, allowing us to investigate the lithium intercalation into graphite over a wide range of intercalation capacity to unveil the underlying mechanisms of lithium plating, diffusion, and discovery of new dense GICs for advanced LIBs with high charging rates and high energy densities.

Insights into the Enhanced Reversibility of Graphite Anode Upon Fast Charging Through Li Reservoir

Increasing the charging rate and reducing the charging time for Li-ion batteries are crucial to realize the mainstream of electric vehicles. However, it is formidable to avoid the Li plating on graphite anode upon fast charging. Despite the tremendous progress in Li detection techniques, the fundamental mechanism of Li plating and its chemical/electrochemical responses upon cycling still remains elusive. Herein, we present a comprehensive electrochemical method to investigate the fast charging behavior of graphite electrode. A detailed analysis is directed toward understanding the changes in phase, composition, and morphology of the fast-charged graphite. By applying a resting process, we scrutinize the further reactions of the plated Li, which readily transforms into irreversible (dead) Li. We further develop a modified graphite electrode with a thin Ag coating as the Li reservoir. The plated Li can be "absorbed" by the Ag layer to form the Li-Ag solid solution that suppresses the formation of dead Li and provides structural stability, thus promoting the further lithiation of graphite and enhancing the reversibility. This work not only provides additional insights into the fast charging behavior of graphite electrode but also demonstrates a potential strategy to improve the fast charging performance of graphite anode.

Dynamic Investigation of Battery Materials via Advanced Visualization: From Particle, Electrode to Cell Level

Li-ion batteries, the most-popular secondary battery, are typically electrochemical systems controlled by ion-insertion dynamics. The battery dynamics involve mass transport, charge transfer, ion-electron coupled reactions, electrolyte penetration, ion solvation, and interfacial evolution. However, it is difficult for the traditional electrochemical methods to capture the accurate and individual details of the dynamic processes in "black box" batteries; instead, only the net result of multi-factors on the whole scale. Recently, different advanced visualization techniques have been developed, which provide powerful tools to track and monitor the internal real-time dynamic processes, giving intuitive details and fine information at various scales from crystal lattice, single particle, electrode to cell level. Here, the recent progress on the investigation of electrochemical dynamics in battery materials are reviewed, via developed techniques across wide timescales and space-scales, including the dynamic process inside the active particle, kinetics issues at the electrode/electrolyte interface, dynamic inhomogeneity in the electrode, and dynamic transportation at the cell level. Finally, the fundamental principles to improve the battery dynamics are summarized and new technologies for future more stringent conditions are highlighted. In prospect, this review opens sight on the battery interior for a clearer, deeper, and more thorough understanding of the dynamics.

Bridging nano- and microscale X-ray tomography for battery research by leveraging artificial intelligence

X-ray computed tomography (CT) is a non-destructive imaging technique in which contrast originates from the materials' absorption coefficient. The recent development of laboratory nanoscale CT (nano-CT) systems has pushed the spatial resolution for battery material imaging to voxel sizes of 50 nm, a limit previously achievable only with synchrotron facilities. Given the non-destructive nature of CT, in situ and operando studies have emerged as powerful methods to quantify morphological parameters, such as tortuosity factor, porosity, surface area and volume expansion, during battery operation or cycling. Combined with artificial intelligence and machine learning analysis techniques, nano-CT has enabled the development of predictive models to analyse the impact of the electrode microstructure on cell performances or the influence of material heterogeneities on electrochemical responses. In this Review, we discuss the role of X-ray CT and nano-CT experimentation in the battery field, discuss the incorporation of artificial intelligence and machine learning analyses and provide a perspective on how the combination of multiscale CT imaging techniques can expand the development of predictive multiscale battery behavioural models.

Nano and Battery Anode: A Review

Improving the anode properties, including increasing its capacity, is one of the basic necessities to improve battery performance. In this paper, high-capacity anodes with alloy performance are introduced, then the problem of fragmentation of these anodes and its effect during the cyclic life is stated. Then, the effect of reducing the size to the nanoscale in solving the problem of fragmentation and improving the properties is discussed, and finally the various forms of nanomaterials are examined. In this paper, electrode reduction in the anode, which is a nanoscale phenomenon, is described. The negative effects of this phenomenon on alloy anodes are expressed and how to eliminate these negative effects by preparing suitable nanostructures will be discussed. Also, the anodes of the titanium oxide family are introduced and the effects of Nano on the performance improvement of these anodes are expressed, and finally, the quasi-capacitive behavior, which is specific to Nano, will be introduced. Finally, the third type of anodes, exchange anodes, is introduced and their function is expressed. The effect of Nano on the reversibility of these anodes is mentioned. The advantages of nanotechnology for these electrodes are described. In this paper, it is found that nanotechnology, in addition to the common effects such as reducing the penetration distance and modulating the stress, also creates other interesting effects in this type of anode, such as capacitive quasi-capacitance, changing storage mechanism and lower volume change.