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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.

Many materials systems comprise complex structures where multiple materials are integrated to achieve a desired performance. Often in these systems, it is a combination of both the materials and their structure that dictate performance. Here the authors layout an integrated computational–statistical–experimental methodology for hierarchical materials systems that takes a holistic design approach to both the material and structure. The authors used computational modeling of the physical system combined with statistical design of experiments to explore an activated carbon adsorption bed. The large parameter space makes experimental optimization impractical. Instead, a computational–statistical approach is coupled with physical experiments to validate the optimization results.

Fixed bed adsorption is an economical method of removing harmful gases, such as SO2 and CO2, from industrial flue gas. It is possible to reduce the cost and environmental impact of fixed bed adsorption by repurposing waste materials to be used as adsorbents, such as semi-coke derived from oil shale, a possible alternative to fossil fuels. Fixed bed adsorption systems are difficult and time consuming to characterize experimentally, especially on large scales. Computational fluid dynamics (CFD) can expand researchers understaning of how these systems are affected by material selection and operating conditions. This study uses CFD to characterize fixed bed adsorption of SO2 and CO2. The research primarily focuses on SO2 adsorption on semi-coke, with an extension to CO2 adsorption on commercial carbon. The CFD modeling was able to describe the amount of the pollutants each material was able to adsorb over time based on a variety of inputs on a larger scale than experimental research. The model was further able to give more detailed comparisons of materials and operating conditions than the experiments, particularly the SO2 and semi-coke system.

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...

In previous work we developed and implemented a sub-grid model for the efficient simulation of heat transfer in gas-particle flows around immersed horizontal cylinders. In this study we apply verification, validation, and uncertainty quantification methods to the developed model to rigorously examine its capabilities and limitations. Numerical verification with small, unit-cell problems shows excellent transient and steady-state behavior. Validation of a bubbling bed and a turbulent bed showed good agreement with high-resolution simulations. To quantify the error of the constitutive model predictions two methods were used to calculate confidence intervals, showing an error of approximately ± 20%, well within the range of typical Nusselt number approximations. The sub-grid model was applied to a conceptual pilot-scale 1 MWe carbon capture reactor to compare with alternative modeling methods. Results show fair predictions of hydrodynamics, heat transfer, and carbon capture rates with significant savings in computational runtimes.

Simulating full-scale heated fluidized bed reactors can provide invaluable insight to the design process. Such simulations are typically computationally intractable due to their complex multi-physics and various length-scales. While it may be possible to simulate some large-scale systems, they require significant computing resources and do not lend themselves well to design optimization methods. To overcome these problems coarse-grid simulations can be used with supplementary constitutive sub-grid models to approximate the unresolved physics. This study details the development, implementation, and verification of a sub-grid model for heat transfer in gas-particle flows with immersed horizontal cylinders. Using the two-fluid model for multiphase flow, small periodic unit-cell domains were simulated over a wide range of flow and geometry conditions. The results were filtered and fit using nonlinear regression to build a Nusselt correlation based on the solids fraction, solids velocity, cylinder geometry (diameter and spacing), and the Peclet number. The proposed model is highly nonlinear and includes power-law contributions from each parameter. The model was verified using a nearly orthogonal experiment design where the input parameters were varied randomly to generate combinations not previously considered. The predicted filtered Nusselt numbers agreed well with the observed (simulated) values. Work is on-going to further expand the capabilities of the model, including 3D simulations, vertical cylinders, and uncertainty quantification.

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.

Studies have shown that the lifetime and performance of lithium batteries are greatly influenced by the charge/discharge cycles of the battery. The number of cycles over which a battery operates depends on the rate and depth of charge/discharge. Degradation in battery performance over multiple cycles is directly related to dendrite growth at the electrode-electrolyte interface in the battery. Understanding how cycling effects dendrite growth rates and morphology requires resolution of the chemical-physical processes at the electrode-electrolyte interface. In this study, a numerical model of dendrite growth at this electrode-electrolyte interface over multiple charge/discharge cycles is presented. In this work, dendrite growth over multiple charge/discharge cycles is simulated at the interfacial level where the effects on dendrite growth rate and morphology can be resolved. The simulations are able to predict qualitative dendrite morphologies presented in experimental studies, and the effects of fast charging scenarios on dendrite growth rate and morphology are discussed.

Dendrite formation on the electrode surface of high energy density lithium (Li) batteries causes safety problems and limits their applications. Suppressing dendrite growth could significantly improve Li battery performance. Dendrite growth and morphology is a function of the mixing in the electrolyte near the anode interface. Most research into dendrites in batteries focuses on dendrite formation in isotropic electrolytes (i.e., electrolytes with isotropic diffusion coefficient). In this work, an anisotropic diffusion reaction model is developed to study the anisotropic mixing effect on dendrite growth in Li batteries. The model uses a Lagrangian particle-based method to model dendrite growth in an anisotropic electrolyte solution. The model is verified by comparing the numerical simulation results with analytical solutions, and its accuracy is shown to be better than previous particle-based anisotropic diffusion models. Several parametric studies of dendrite growth in an anisotropic electrolyte are performed and the results demonstrate the effects of anisotropic transport on dendrite growth and morphology, and show the possible advantages of anisotropic electrolytes for dendrite suppression.