All-solid-state lithium metal batteries, poised to deliver one of the highest specific energy among rechargeable batteries, establish themselves as a front-runner for next-generation electric vehicle batteries; nevertheless, realizing high-performance solid-state Li batteries remains challenging due to the high impedance and (for the anode side) uncontrolled Li deposition at the interfaces between active electrode materials and solid electrolytes. The lack of direct and quantitative characterization tools for the active material−electrolyte interfaces has been a major hurdle towards high-performance solid-state batteries. Most studies had to rely on indirect characterization methods to approximate the identity, distribution, and evolution of species and structures at the interfaces. This work demonstrates a combined diagnostics of the interfaces and their evolutions during cell operation using in situ multi-site plasma focused-ion-beam (pFIB) tomography. Utilizing the advanced capabilities of pFIB technology, which offers significantly higher material removal rates compared to gallium-based systems, we significantly reduce the duration of tomography sessions. This efficiency enhancement not only accelerates the morphological data acquisition but also supports the continuous and multi-site application of pFIB tomography on a single specimen through its electrochemical reaction cycle. In this experiment, the Li-electrolyte interface within a trilayer battery structure (Li/electrolyte/NMC-electrolyte composite) was characterized in situ at multiple sites using FIB. The tests were performed in a custom-built transfer vessel equipped with an in situ testing platform, enabling air-free specimen transfer from an argon-filled glovebox to the pFIB-SEM setup. This system also facilitates in situ charge/discharge cycling under simulated real-world conditions, including environmental stimuli like heating and pressing. The open-cell design of the battery specimen on this platform, with exposed and polished interfaces, provides direct visibility into the interface dynamics that is comparable to traditional Swagelok-cell setups but with enhanced observational capabilities. The resultant tomography models from the Li-electrolyte interface during charging and discharging cycles elucidate the structural evolution of the components under different operational statuses. Key observations include the initiation, growth, and interaction of Li dendrites within the electrolyte layer, and the development of voids at the Li-electrolyte interface, particularly their dynamics during battery operation. While these phenomena have been extensively studied using other diagnostic techniques, the application of in situ multi-site pFIB offers unprecedented insights into the morphological changes induced by battery cycling, thereby contributing to a comprehensive understanding of the failure mechanisms in solid-state batteries.

The scarcity of high-quality data presents a major challenge to the prediction of material properties using machine learning (ML) models. Obtaining material property data from experiments is economically cost-prohibitive, if not impossible. In this work, we address this challenge by generating an extensive material property dataset comprising thousands of data points pertaining to the elastic properties of Fe-C alloys. The data were generated using molecular dynamic (MD) calculations utilizing reference-free Modified embedded atom method (RF-MEAM) interatomic potential. This potential was developed by fitting atomic structure-dependent energies, forces, and stress tensors evaluated at ground state and finite temperatures using ab-initio. Various ML algorithms were subsequently trained and deployed to predict elastic properties. In addition to individual algorithms, super learner (SL), an ensemble ML technique, was incorporated to refine predictions further. The input parameters comprised the alloy’s composition, crystal structure, interstitial sites, lattice parameters, and temperature. The target properties were the bulk modulus and shear modulus. Two distinct prediction approaches were undertaken: employing individual models for each property prediction and simultaneously predicting both properties using a single integrated model, enabling a comparative analysis. The efficiency of these models was assessed through rigorous evaluation using a range of accuracy metrics. This work showcases the synergistic power of MD simulations and ML techniques for accelerating the prediction of elastic properties in alloys.

Petroleum coke (PetCoke) is a byproduct of oil refining that is commonly used as a heating resource and contributing to carbon emissions. Additionally, the presence of heavy metals and sulfur in PetCoke has also raised significant environmental concerns. Moreover, traditional high-temperature PetCoke treatment methods exacerbate greenhouse gas emissions. This study introduces an energy-efficient approach for converting PetCoke into carbon anode materials for solid-state batteries. Utilizing a custom-built laser-based vapor deposition setup, comprising a tube furnace and a nanosecond-pulsed infrared laser (1064 nm), we explored the PetCoke transfer process. Subjecting PetCoke to high power density laser irradiation generates localized high temperatures (~3000°C), initiating sample evaporation. By modulating the local conditions within the tube furnace, e.g., temperature, pressure, and argon flow rate, we can precisely control the kinetic gradient between the PetCoke and the plating substrate. This control is crucial for transforming PetCoke into various carbon forms with tailored microstructure and morphology, while selectively removing sulfur and heavy metals due to their differing nucleation rates on copper foil. One outcome of this process is the production of high-purity carbon black (C65) with a specific surface area of 400 m2/g, as verified by Brunauer-Emmett-Teller (BET) analysis. The exceptional surface area of C65 facilitates its use in creating a mixed-ionic-electronic conductor (MIEC) layer within all-solid-state batteries, enhancing lithium plating and stripping efficiency during cycling. Our battery design, incorporating C65/Ag/Solid Electrolyte/NMC811 and electrolyte composite, leverages laser-induced evaporation for C65 deposition on copper foil and employs Li6PS5Cl as the solid electrolyte. The resulting solid-state cell exhibits remarkable electrochemical performance, maintaining operation at an impressive current density of 2.4 mA/cm2 and achieving an average coulombic efficiency of 99.5% over 200 cycles.

All-solid-state batteries with lithium metal anodes hold great potential for high-energy battery applications. However, forming and maintaining stable solid-solid contact between the lithium anode and solid electrolyte remains a major challenge. One promising solution is the use of a silver-carbon (Ag-C) interlayer, but its chemomechanical properties and impact on interface stabilities need to be comprehensively explored. Here, we examine the function of Ag-C interlayers in addressing interfacial challenges using various cell configurations. Experiments show that the interlayer improves interfacial mechanical contact, leading to a uniform current distribution and suppressing lithium dendrite growth. Furthermore, the interlayer regulates lithium deposition in the presence of Ag particles via improved Li diffusivity. The sheet-type cells with the interlayer achieve a high energy density of 514.3 Wh L-1 and an average Coulombic efficiency of 99.97% over 500 cycles. This work provides insights into the benefits of using Ag-C interlayers for enhancing the performance of all-solid-state batteries.

One of the major impediments to the computational investigation and design of complex alloys such as steel is the lack of effective and versatile interatomic potentials to perform large-scale calculations. In this study, we developed an RF-MEAM potential for the iron-carbon (Fe-C) system to predict the elastic properties at elevated temperatures. Several potentials were produced by fitting potential parameters to the various datasets containing forces, energies, and stress tensor data generated using density functional theory (DFT) calculations. The potentials were then evaluated using a two-step filter process. In the first step, the optimized RSME error function of the potential fitting code, MEAMfit, was used as the selection criterion. In the second step, molecular dynamics (MD) calculations were employed to calculate ground-state elastic properties of structures present in the training set of the data fitting process. The calculated single crystal and poly-crystalline elastic constants for various Fe-C structures were compared with the DFT and experimental results. The resulting best potential accurately predicted the ground state elastic properties of B1, cementite, and orthorhombic-Fe7C3 (O-Fe7C3), and also calculated the phonon spectra in good agreement with the DFT-calculated ones for cementite and O-Fe7C3. Furthermore, the potential was used to successfully predict the elastic properties of interstitial Fe-C alloys (FeC-0.2% and FeC-0.4%) and O-Fe7C3 at elevated temperatures. The results were in good agreement with the published literature. The successful prediction of elevated temperature properties of structures not included in data fitting validated the potential's ability to model elevated-temperature elastic properties.

The understanding of lithium (Li) nucleation and growth is important to design better electrodes for high-performance batteries. However, the study of Li nucleation process is still limited because of the lack of imaging tools that can provide information of the entire dynamic process. We developed and used an operando reflection interference microscope (RIM) that enables real-time imaging and tracking the Li nucleation dynamics at a single nanoparticle level. This dynamic and operando imaging platform provides us with critical capabilities to continuously monitor and study the Li nucleation process. We find that the formation of initial Li nuclei is not at the exact same time point, and Li nucleation process shows the properties of both progressive and instantaneous nucleation. In addition, the RIM allows us to track the individual Li nucleus’s growth and extract spatially resolved overpotential map. The nonuniform overpotential map indicates that the localized electrochemical environments substantially influence the Li nucleation.

As an interlayer between the anode and the electrolyte of the all-solid-state lithium metal batteries (ASSLMBs), the silver-carbon (Ag-C) nanocomposite has been reported to significantly increase the energy density and cycle rate of solid-state lithium metal batteries. Ag-C interlayers serve as mixed ionic-electronic conductor that conducts both Li+ ions and electrons and lithium storage capacity. Unfortunately, it was unclear how the Ag-C interlayer regulated lithium plating and stripping. Moreover, the structural and chemical instabilities between the interlayer and the electrolyte, within the interlayer, or beneath the interlayer on lithium substrate are likely to cause cell failure. In this review, we discuss interfacial issues and summarize recent progress in solution strategies for ASSLMBs, with a specific focus on the use of a silver-carbon (Ag-C) nanocomposite interlayer in anode-free setups. Based on the Li transport kinetics among the Ag-C interlayers, the interfacial configurations of Ag-C interlayers are classified as either exterior or internal. The review concludes with a discussion of the perspectives and future prospects, allowing for the improvement of interlayer techniques for solid-state batteries.