Characterization of fabrication-microstructure-property relationships for polymer-based battery materials, combining tomographic 3D imaging with modeling and simulation

电池电极的三维形态对其性能和退化至关重要。确保同时离子和电导率在整个电极需要一个稳定的层次三维结构在微观和纳米尺度。因此，详细了解电极的形态及其对电极功能特性的影响对于提高聚合物基电池的电化学性能是必不可少的。该项目旨在了解如何设计具有优化3D形态的聚合物基电池电极，从而提高电化学性能，包括降解稳定性。在第一个资助期成果的基础上，SPP 2248项目中由不同合作伙伴提供的各种材料将使用聚焦离子束（FIB）断层扫描、基于同步加速器的微断层扫描以及纳米断层扫描（除了第一个资助期外）进行测量。现在将特别关注operando测量，以更好地理解由循环老化引起的电解质填充和降解现象。然后在UU对HZB的3D和4D图像数据进行统计分析，以量化形态学变化。此外，使用随机几何工具，将分割的图像数据作为开发电极形态参数化随机模型的基础，从而允许生成数字双胞胎，即模拟与测量电极统计等效的结构。通过系统地改变模型参数，将产生广泛的虚拟的，但现实的电极结构，其成本仅为计算机模拟，这还没有被制造出来。这为广泛的虚拟场景分析奠定了基础，称为虚拟材料测试。层析测量以及模拟电极形态，然后用作HSU的电化学性质的空间分辨物理模拟的输入。通过将制造参数的知识与所得到的电极形态和相应的电化学性能相结合，将建立定量的制造-微观结构-性能关系。通过这种方式，3D和4D成像方法与数据驱动的建模和仿真方法相结合，可以生成结构建议，从而支持设计具有优化电化学性能的聚合物基电池电极。

The 3D morphology of battery electrodes is vital for their performance and degradation. Ensuring simultaneous ionic and electric conductivity throughout the electrode requires a stable hierarchical 3D structure on micro- and nanometer scales. Thus, a detailed knowledge of the morphology and its impact on the functional properties of electrodes is indispensable for improving the electrochemical performance of polymer-based batteries. This project aims to understand how to design polymer-based battery electrodes with an optimized 3D morphology, which leads to an improved electrochemical performance including degradation stability. Building on the results of the first funding period, a wide variety of materials provided by various partners within SPP 2248 will be measured using focused ion beam (FIB) tomography, synchrotron-based micro-tomography and, in addition to the first funding period, nano-tomography. Particular attention will now be paid to operando measurements to better understand electrolyte filling and degradation phenomena caused by cyclic aging. The 3D and 4D image data from HZB is then analyzed statistically at UU to quantify morphological changes. Moreover, segmented image data is used as basis for developing parametric stochastic models of electrode morphology using tools from stochastic geometry, which allow for the generation of digital twins, i.e., the simulation of structures that are statistically equivalent to the measured electrodes. By a systematic variation of the model parameters, a wide spectrum of virtual, but realistic electrode structures will be generated just at the cost of computer simulations, which have not yet been manufactured. This lays ground for extensive virtual scenario analyses, called virtual materials testing. Tomographically measured as well as simulated electrode morphologies are then used as an input for spatially-resolved physics-based simulations of electrochemical properties at HSU. By combining the knowledge of the manufacturing parameters with the resulting electrode morphology and the corresponding electrochemical performance, quantitative fabrication-microstructure-property relationships will be established. In this way, methods from 3D and 4D imaging in combination with a data-driven modeling and simulation approach allows for the generation of structuring recommendations that support the design of polymer-based battery electrodes with an optimized electrochemical performance.