NMR studies of activation energy distributions in solid ion conductors

固体材料中的离子输运对于实现和完善现代应用具有重要意义。例如，改进现有电池技术是一个关键因素，因此，克服目前与能量储存和转换有关的限制。为了根据特定功能的要求定制离子传输，对固体电解质中的结构-动力学关系的基本理解是至关重要的。在这方面，能源景观是一个富有成果的概念。该项目利用核磁共振的能力来建立离子动力学和固体电解质能量景观之间的联系。特别是，各种实验技术，包括场循环弛豫，受激回波实验，和场梯度扩散，结合起来，以确定锂离子动力学和能量在不同的时间和长度尺度。这种分析涉及无定形和结晶电解质，使我们能够实现很大程度上不同程度的秩序。结果，例如，相关的时间和活化能分布，与调查结果的多方面的实验和理论方法的动力学和结构，这些材料在其他项目的合作研究工作，特别是，从P1的扩散系数分布，原子探针断层扫描数据从P3，从P5的理论模型。通过这种方式，我们确定宏观离子输运如何从各种类型的能量景观中的基本离子跳跃演变。这些信息为基于知识的应用相关材料属性操作铺平了道路。

Ion transport in solid materials is of great importance to enable and refine modern applications. For example, it is a key factor to improve existing battery technologies and, hence, to overcome current limitations relating to the storage and conversion of energy. To tailor the ion transport to the requirements for specific functions, a fundamental understanding of the structure-dynamics relations in solid electrolytes is crucial. Energy landscapes are a fruitful concept in this context. This project uses the capabilities of nuclear magnetic resonance to establish links between ion dynamics and energy landscapes of solid electrolytes. In particular, a variety of experimental techniques, including field-cycling relaxometry, stimulated-echo experiments, and field-gradient diffusometry, are combined to ascertain lithium ion dynamics and energetics on various time and length scales. This analysis involves both amorphous and crystalline electrolytes allowing us to realize largely different degrees of order. The results, e.g., correlation time and activation energy distributions, are compared with findings of manifold experimental and theoretical approaches to the dynamics and structure of these materials obtained in other projects of a collaborative research effort, in particular, with distributions of diffusion coefficients from P1, atom probe tomography data from P3, and theoretical models from P5. In this way, we determine how macroscopic ion transport evolves from elementary ion jumps in various types of energy landscapes. This information paves the way for knowledge-based manipulation of application-relevant material properties.