Characterization and Quantification of Depletion and Accumulation Layers in Solid-State Li+ -Conducting Electrolytes Using In Situ Spectroscopic Ellipsometry

Accelerated Li Penetration and Crack Propagation Due to Mechanical Degradation of Sulfide-Based Solid Electrolyte

This work presents quantitative investigations into the relationships between lithium dendrite growth in the defects of Li6PS5Cl (LPSCl) solid electrolyte (SE), crack nucleation and propagation in the SE, and the associated mechanical forces driving these dendrites and cracks. Two different growth modes for lithium dendrites are identified by ex situ scanning electron microscopy (SEM) observation: longitudinal cracking inside pores in the SE and lateral penetration along boundaries of the SE particles. These in situ TEM tests reveal that concentrated Li plating in a nano-sized defect on the LPSCl surface will lead to the nucleation and propagation of cracks into the LPSCl under a stress much smaller than the expected mechanical strength of the LPSCl material. This unexpected mechanical degradation is caused by a reduction in the mechanical strength of LPSCl during electrochemical charge/discharge cycling, resulting from a disorder in the crystal structure of LPSCl as revealed by DFT simulations. Due to this mechanical degradation of LPSCl, the threshold force necessary to initiate crack growth is much lower than the previously expected force to drive dendrite growth.

Monitoring the Electrochemical Failure of Indium Tin Oxide Electrodes via Operando Ellipsometry Complemented by Electron Microscopy and Spectroscopy

Transparent conductive oxides such as indium tin oxide (ITO) are standards for thin film electrodes, providing a synergy of high optical transparency and electrical conductivity. In an electrolytic environment, the determination of an inert electrochemical potential window is crucial to maintain a stable material performance during device operation. We introduce operando ellipsometry, combining cyclic voltammetry (CV) with spectroscopic ellipsometry, as a versatile tool to monitor the evolution of both complete optical (i.e., complex refractive index) and electrical properties under wet electrochemical operational conditions. In particular, we trace the degradation of ITO electrodes caused by electrochemical reduction in a pH-neutral, water-based electrolyte environment during electrochemical cycling. With the onset of hydrogen evolution at negative bias voltages, indium and tin are irreversibly reduced to the metallic state, causing an advancing darkening, i.e., a gradual loss of transparency, with every CV cycle, while the conductivity is mostly conserved over multiple CV cycles. Post-operando analysis reveals the reductive (loss of oxygen) formation of metallic nanodroplets on the surface. The reductive disruption of the ITO electrode happens at the solid-liquid interface and proceeds gradually from the surface to the bottom of the layer, which is evidenced by cross-sectional transmission electron microscopy imaging and complemented by energy-dispersive X-ray spectroscopy mapping. As long as a continuous part of the ITO layer remains at the bottom, the conductivity is largely retained, allowing repeated CV cycling. We consider operando ellipsometry a sensitive and nondestructive tool to monitor early stage material and property changes, either by tracing failure points, controlling intentional processes, or for sensing purposes, making it suitable for various research fields involving solid-liquid interfaces and electrochemical activity.

Utilizing Data-Driven Optimization to Automate the Parametrization of Kinetic Monte Carlo Models

Kinetic Monte Carlo (kMC) simulations are a popular tool to investigate the dynamic behavior of stochastic systems. However, one major limitation is their relatively high computational costs. In the last three decades, significant effort has been put into developing methodologies to make kMC more efficient, resulting in an enhanced runtime efficiency. Nevertheless, kMC models remain computationally expensive. This is in particular an issue in complex systems with several unknown input parameters where often most of the simulation time is required for finding a suitable parametrization. A potential route for automating the parametrization of kinetic Monte Carlo models arises from coupling kMC with a data-driven approach. In this work, we equip kinetic Monte Carlo simulations with a feedback loop consisting of Gaussian Processes (GPs) and Bayesian optimization (BO) to enable a systematic and data-efficient input parametrization. We utilize the results from fast-converging kMC simulations to construct a database for training a cheap-to-evaluate surrogate model based on Gaussian processes. Combining the surrogate model with a system-specific acquisition function enables us to apply Bayesian optimization for the guided prediction of suitable input parameters. Thus, the amount of trial simulation runs can be considerably reduced facilitating an efficient utilization of arbitrary kMC models. We showcase the effectiveness of our methodology for a physical process of growing industrial relevance: the space-charge layer formation in solid-state electrolytes as it occurs in all-solid-state batteries. Our data-driven approach requires only 1-2 iterations to reconstruct the input parameters from different baseline simulations within the training data set. Moreover, we show that the methodology is even capable of accurately extrapolating into regions outside the training data set which are computationally expensive for direct kMC simulation. Concluding, we demonstrate the high accuracy of the underlying surrogate model via a full parameter space investigation eventually making the original kMC simulation obsolete.

Mass transport and charge transfer through an electrified interface between metallic lithium and solid-state electrolytes

All-solid-state Li-ion batteries are one of the most promising energy storage devices for future automotive applications as high energy density metallic Li anodes can be safely used. However, introducing solid-state electrolytes needs a better understanding of the forming electrified electrode/electrolyte interface to facilitate the charge and mass transport through it and design ever-high-performance batteries. This study investigates the interface between metallic lithium and solid-state electrolytes. Using spectroscopic ellipsometry, we detected the formation of the space charge depletion layers even in the presence of metallic Li. That is counterintuitive and has been a subject of intense debate in recent years. Using impedance measurements, we obtain key parameters characterizing these layers and, with the help of kinetic Monte Carlo simulations, construct a comprehensive model of the systems to gain insights into the mass transport and the underlying mechanisms of charge accumulation, which is crucial for developing high-performance solid-state batteries.

Li+ Conductivity of Space Charge Layers Formed at Electrified Interfaces Between a Model Solid-State Electrolyte and Blocking Au-Electrodes

The formation of space charge layers in solid-state ion conductors has been investigated as early as the 1980s. With the advent of all-solid-state batteries as an alternative to traditional Li-ion batteries, possibly improving performance and safety, the phenomenon of space charge formation caught the attention of researchers as a possible origin for the observed high interfacial resistance. Following classical space charge theory, such high resistances result from the formation of the depletion layers. These layers of up to hundreds of nanometers in thickness are almost free of mobile cations. With the prediction of a Debye-like screening effect, the thickness of the depletion layer is expected to scale with the square root of the absolute temperature. In this work, we studied the temperature dependence of the depletion layer properties in model solid Ohara LICGC Li⁺ conducting electrolytes using electrochemical impedance spectroscopy. We show that the activation energy inside the depletion layer increases to ca 0.42 eV compared to ca 0.39 eV in the bulk electrolyte. Moreover, the proportionality between temperature and depletion layer thickness, correlating to the Debye length, is tested and validated.

Hydration of LiOH and LiCl-Near-Infrared Spectroscopic Analysis

The hydration behavior of LiOH, LiOH·H2O, and LiCl was observed by near-infrared (NIR) spectroscopy. Anhydrous LiOH showed two absorption bands at 7340 and 7171 cm-1. These NIR bands were assigned to the first overtone of surface hydroxyls and interlayer hydroxyls of LiOH, respectively. LiOH·H2O showed two absorption bands at 7137 and 6970 cm-1. These NIR bands were assigned to the first overtone of interlayer hydroxyls and H2O molecules coordinated with Li+, respectively. The interlayer OH- and the coordinated H2O of LiOH·H2O were not modified even when the LiOH·H2O was exposed to air. In contrast, anhydrous LiOH was slowly hydrated for several hours, to form LiOH·H2O under ambient conditions (RH 60%). Kinetic analysis showed that the hydration of the interlayer OH- of LiOH proceeded as a second-order reaction, indicating the formation of intermediate species-[Li(H2O) x (OH)4]3- (x = 1 or 2). However, the hydration of the LiOH surface did not follow a second-order reaction because the chemisorption of H2O molecules onto the defect sites of the LiOH surface does not need to crossover the energy barrier. Furthermore, we succeeded in observing the hydration of deliquescent LiCl, including the formation of LiCl solution for several minutes by NIR spectroscopy.

Review on physical impedance models in modern battery research

Electrochemical impedance spectroscopy (EIS) is a versatile tool to understand complex processes in batteries. This technique can investigate the effects of battery components like the electrode and electrolyte, electrochemical reactions, interfaces, and interphases forming in the electrochemical systems. The interpretation of the EIS data is typically made using models expressed in terms of the so-called electrical equivalent circuits (EECs) to fit the impedance spectra. Therefore, the EECs must unambiguously represent the electrochemistry of the system. EEC models with a physical significance are more relevant than the empirical ones with their inherent imperfect description of the ongoing processes. This review aims to present the readers with the importance of physical EEC modeling within the context of battery research. A general introduction to EIS and EEC models along with a brief description of the mathematical formalism is provided, followed by showcasing the importance of physical EEC models for EIS on selected examples from the research on traditional, aqueous, and newer all-solid-state battery systems.