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

电池电极的3D形态对其性能和退化至关重要。确保整个电极同时具有离子和导电性需要在微米和纳米尺度上具有稳定的分层3D结构。因此，详细了解电极的形态及其对功能特性的影响对于改善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的3D形态的聚合物基电池电极，从而改善电化学性能，包括降解稳定性。在第一个资助期的基础上，SPP 2248内的各种合作伙伴提供的各种材料将使用聚焦离子束（FIB）断层扫描，基于同步加速器的显微断层扫描以及纳米断层扫描进行测量。现在将特别注意操作性测量，以更好地理解电解质填充和循环老化引起的降解现象。然后在UU对来自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，分割的图像数据被用作使用来自随机几何学的工具开发电极形态的参数随机模型的基础，其允许生成数字孪生，即，在统计学上等同于被测电极的结构的模拟。通过模型参数的系统变化，将仅以计算机模拟为代价产生宽范围的虚拟但真实的电极结构，其尚未被制造。这为广泛的虚拟场景分析奠定了基础，称为虚拟材料测试。断层扫描测量以及模拟的电极形态，然后被用作输入空间分辨的物理为基础的模拟在HSU的电化学性能。通过将制造参数的知识与所得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，来自3D和4D成像的方法与数据驱动的建模和模拟方法相结合，允许生成结构化建议，这些建议支持具有优化电化学性能的聚合物基电池电极的设计。

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.