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materials, and provide ways to scale up for commercial production. Computational approaches can predict structures of both stable and metastable compounds with desired properties, but the synthesis route is often too expensive or not known. To solve this problem there is a need for the in situ monitoring of synthesis. Some work has been done in various fields, such as mesoporous materials,66 oxides, and phosphides using different types of synthesis procedures,67 as well as in situ monitoring of crystallization pathways (Figure 3.4.6) oriented toward controlled synthesis.68-70 Figure 3.4.6. Schematics of two-dimensional layer growth (A) and three-dimensional island growth (D), depicting proposed mechanisms of silicalite-1 crystallization at high and low temperature, respectively. (B and C) Scanning electron microscope images of crystals prepared by either seeded (E) or nonseeded (F) crystallization, which exhibit rough surfaces and spheroidal shapes. From Ref. 70. Reproduced with permission of AAAS. The Ultimate Goal: Prediction of Materials Structure and Behavior: Computation and modeling have over the last decade become essential tools in advancement of energy storage technologies, providing a mechanistic rationale to guide material, component, and device design. Modeling approaches applied to electrochemical energy storage systems range across electronic structure methods, atomistic simulations, microstructural simulations, and continuum-scale reaction and transport models. Computational materials screening, based on first-principles electronic structure methods, has been applied to discover new materials with desired properties for battery components as well as in electrocatalysis, heterogeneous catalysis, and photocatalysis. When it comes to batteries there have been a number of computational atomic-scale predictions of electrode materials and electrolytes based on the Materials Genome approach. See for example the recent reviews.71,72 First-principles methods can be used to predict discharge voltages, 73,74 ion diffusion rates, and material stability by calculating bulk phase diagrams71 and surface reactivity.28 First-principles computational studies of electrolytes can determine electrochemical stability windows and ion-association energies to identify potential new formulations.75 History-dependent lithiation algorithms have captured key aspects of the transition of crystalline-Si surfaces to amorphous-Si surfaces during electrochemical lithiation (Figure 3.4.7).76 However, the characterization of the state of the electrode surface in direct contact with the electrolyte and its evolution during battery cycling still remains a challenge. There have also been important advances in modeling phenomena at longer length and time scales. Macro- homogeneous approaches in modeling battery electrodes, 77 as well as coarse-grained mesoscopic 78 and continuum-scale models,79 have become important tools in understanding how materials properties can translate into battery behavior and performance. Over the last decade, there have been many efforts in the area of continuum modeling. Currently, multi-physics, multi-scale models coupling electrochemical-thermal-electrical simulation tools are being developed and used by the battery community to predict performance of cells and packs.80 These models span architectures from the electrode to the cell level to modules to packs and can even be linked to vehicle performance. Efforts are also underway to link mechanical and structural models to these electrochemical-thermal-electrical models to predict the safety behavior of electrodes, cells, modules, and packs81 under a crash-induced crush. NEXT GENERATION ELECTRICAL ENERGY STORAGE PANEL 4 REPORT 121PDF Image | Next Generation Electrical Energy Storage
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