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Lithium metal as an anode has been widely accepted due to its higher negative electrochemical potential and theoretical capacity. Nevertheless, the existing safety and cyclability issues limit lithium metal anodes from practical use in high-energy density batteries. Repeated Li deposition and dissolution processes upon cycling lead to the formation of dendrites at the interface which results in reduced Li availability for electrochemical reactions, disruption in Li transport through the interface and increased safety concerns due to short circuiting. Here, we demonstrate a novel strategy using Ionic Liquid Crystals (ILCs) as the electrolyte cum pseudo-separator to suppress dendrite growth with their anisotropic properties controlling Li-ion mass transport. A thermotropic ILC with two-dimensional Li-ion conducting pathways was synthesized and characterized. Microscopic and spectroscopic analyses elucidate that the ILC formed with a smectic A phase, which can be utilized for wide temperature window operation. The results of electrochemical studies corroborate the efficacy of ILC electrolytes in mitigating dendrite formation even after 850 hours and it is further substantiated by numerical simulation and the mechanism involved in dendritic suppression was deduced.

Because of its ultrahigh specific capacity, lithium metal holds great promise for revolutionizing current rechargeable battery technologies. Nevertheless, the unavoidable formation of dendritic Li, as well as the resulting safety hazards and poor cycling stability, have significantly hindered its practical applications. A mainstream strategy to solve this problem is introducing porous media, such as solid electrolytes, modified separators, or artificial protection layers, to block Li dendrite penetration. However, the scientific foundation of this strategy has not yet been elucidated. Herein, using experiments and simulation we analyze the role of the porous media in suppressing dendritic Li growth and probe the underlying fundamental mechanisms. It is found that the tortuous pores of the porous media, which drastically reduce the local flux of Li+ moving toward the anode and effectively extend the physical path of dendrite growth, are the key to achieving the nondendritic Li growth. On the basis of the t...

The formation of dendrites on the anode surface of metal air batteries, such as the Li-air battery, causes significant decreases in performance over the lifetime of the battery and poses safety concerns due to short circuiting. Predictive computational methods are used to investigate novel electrolyte materials which will reduce dendrite growth, in particular liquid crystal materials for Li-air electrolytes. The literature on liquid crystal electrolytes was surveyed for materials data and used to develop a training set of liquid crystal compounds. These compounds were then used as a knowledge base to develop property structure relations for the clearing and melting temperature for liquid crystals, which are a critical design parameter for the design of novel electrolytes. This was accomplished using standard artificial neural networks trained on molecular fingerprints and convolutional neural networks (CNNs) trained on compound images. Transfer learning was also demonstrated to boost predictive performance through pre-training neural networks on larger pre-existing compound datasets. The results show that CNNs achieve comparable accuracy compared to molecular fingerprints, and that both multilayer perceptrons and CNNs can benefit from transfer learning. This study is the first where transfer learning in CNNs aids in the prediction of one experimental property in a small data regime, using data of another experimental property.

Dendrite formation on the anode surface of a battery is closely related to the safety and capacity of high energy density batteries, thus suppressing dendrite growth will significantly improve the performance of batteries. Many experimental reports reveal that convection near the dendrite nucleation site can change the local mass transport, and ultimately affect dendrite growth. Investigation of the convection effect in batteries will guide the development of strategies to suppress dendrite growth in a convective electrolyte. Most of the existing electro-convection computational models for dendrite growth studies are based on Eulerian frameworks. These methods have difficulty modeling the moving boundaries associated with dendrite growth and are less computationally efficient in simulating convective fluid motion. In this paper we adopt a mesh-free particle based Lagrangian method to address the challenges of previous grid based Eulerian electro-convection models. The developed model is verified by comparison to analytical solutions, including verification of ion migration and the electric potential. Simulation results show that the predicted dendrite growth and electro-convective flow patterns compare well with experimental results during early dendrite growth stages. Parametric studies reveal that low viscosity electrolytes suppress the dendrite growth by increasing the mass transport of ions near the anode/electrolyte interface.

Dendrite growth is an ongoing challenge on the anode surface in lithium metal batteries. Over multiple charge and discharge cycles dendritic growth leads to a decrease in battery performance and increased safety issues due to short circuiting. The morphology and growth rate of dendrites depend on a number of factors including the rate and depth of charge/discharge. In this work a computational model of dendrite growth at the interface is used to investigate the effects of cycling on dendrite growth rate and morphology. The model resolves the interfacial chemical-physical processes and simulates multiple charge and discharge cycles. Results show that dendrite growth depends on both the charging and discharging cycles, which can lead to heterogeneous growth patterns based on the levels of deposition and dissolution of lithium during the cycles.

In this paper we investigate transport limitations in the electrodes of lithium-air batteries through computational modeling. We use meso-scale models to consider the effects of dendrites on the current and potential at the anode surface, and to investigate the effects of reaction and transport parameters on the formation of precipitates in the cathode. The formation of dendrites on the anode surface during cycling reduces the transport of ions and can lead to short circuits in the cell. Growth of precipitates in the cathode reduces the specific capacity of the cell due to surface passivation and pore clogging. Both of these degradation mechanisms depend on meso-scale phenomena, such as the pore-scale reactive transport in the cathode. To understand the effects of the meso-scale transport and precipitation on the performance and lifetime of Li-air batteries, meso-scale modeling is needed that is able to resolve the electrodes and their microstructures.

Over their designed lifetime, high-temperature electrochemical devices, such as solid oxide fuel cells (SOFCs), can experience degradation in their electrochemical performance due to environmental conditions, operating conditions, contaminants, and other factors. Understanding the different degradation mechanisms in SOFCs and other electrochemical devices is essential to reducing performance degradation and increasing the lifetimes of these devices. In this paper, SOFC degradation mechanisms are evaluated, and a damage model is presented that describes performance degradation in SOFCs due to damage or degradation in the SOFC electrodes. A degradation classification scheme is presented, dividing the various SOFC electrode degradation mechanisms into categories based on their physical effects on the SOFC. The damage model and classification method are applied both to sulfur poisoning and antimony poisoning, which occur in the SOFC anode. For sulfur poisoning, the model can calculate degradation in SOFC performance based on the operating temperature of the fuel cell and the concentration of gaseous sulfur species in the anode. For antimony poisoning, the effects of nickel consumption from the anode matrix are investigated.

Solid oxide fuel cells (SOFCs) are high temperature fuel cells, which are being developed for large scale and distributed power systems. SOFCs promise to provide cleaner, more efficient electricity than traditional fossil fuel burning power plants. Research over the last decade has improved the design and materials used in SOFCs to increase their performance and stability for long-term operation; however, there are still challenges for SOFC researchers to overcome before SOFCs can be considered competitive with traditional fossil fuel burning and renewable power systems. In particular degradation due to contaminants in the fuel and oxidant stream is a major challenge facing SOFCs. In this paper we discuss ongoing computational and experimental research into different degradation and design issues in SOFC electrodes. We focus on contaminants in gasified coal which cause electrochemical and structural degradation in the anode, and chromium poisoning which affects the electrochemistry of the cathode. Due to the complex microstructures and multi-physics of SOFCs, multi-scale computational modeling and experimental research is needed to understand the detailed physics behind different degradation mechanisms, the local conditions within the cell which facilitate degradation, and its effects on the overall SOFC performance. We will discuss computational modeling research of SOFCs at the macro-, meso- and nano-scales which is being used to investigate the performance and degradation of SOFCs. We will also discuss the need for a multi-scale modeling framework of SOFCs, and the application of computational and multi-scale modeling to several degradation issues in SOFCs.

A cell-level distributed electrochemistry (DEC) modeling tool has been developed to enable predicting trends in solid oxide fuel cell performance by considering the coupled and spatially varying multi-physics that occur within the tri-layer. The approach calculates the distributed electrochemistry within the electrodes, which includes the charge transfer and electric potential fields, ion transport throughout the tri-layer, and gas distributions within the composite and porous electrodes. The thickness of the electrochemically active regions within the electrodes is calculated along with the distributions of charge transfer. The DEC modeling tool can examine the overall SOFC performance based on electrode microstructural parameters, such as particle size, pore size, porosity, electrolyte- and electrode-phase volume fractions, and triple-phase-boundary length. Recent developments in electrode fabrication methods have lead to increased interest in using graded and nano-structured electrodes to improve the electrochemical performance of SOFCs. This paper demonstrates how the DEC modeling tool can be used to help design novel electrode microstructures by optimizing a graded anode for high electrochemical performance.