Collaborative Research: High-Throughput Quantification of Solid State Electrochemistry for Next Generation Energy Technologies

非技术描述：这项研究的目标是促进对燃料电池、电解电池、电池和其他能源技术中使用的氧化物电极行为的基本理解。该方法结合了材料结构库的高通量合成，先进的高通量表征和高通量数据分析。通过利用具有明确几何特征的结构，可以直接解释电化学数据。反过来，提供的洞察力使深思熟虑的结构工程能够实现卓越的性能。它还为如何创建下一代材料提供了化学指导。性能的提高最终可以推进可持续能源的目标。通过为高中生和本科生提供实习机会，以及为研究生提供博士研究机会，将各个层次的学生广泛纳入这项工作的研究和培训目标。拓展工作包括让当地K-12学生参与科学和工程。技术描述：这项工作旨在通过利用几何上定义良好的系统，显著地推进对电化学反应途径的理解。典型的电化学结构包含随机的、高表面积的特征，以最大限度地提高整体性能，不太适合提取基本行为。相比之下，几何定义良好的系统可以确定诸如长度比三相边界活度，散装化学扩散系数，面积比表面活度等性质。这些是高性能结构工程设计的基本参数。然而，尽管越来越多的人认识到电化学系统的价值，但由于使用单独制备的样品获取这些数据的艰苦性质，使得对几何上定义良好的电化学系统的研究仅限于几个重要的例子。在这个项目中，利用先进的制造工具在电解质衬底上创建电极结构库，并使用内部构建的独特扫描电化学探针系统快速测量每个库的全部内容。计算工具的开发是为了处理生成的大量数据，包括数据挖掘和机器学习功能，以提高数据采集和分析的效率。几何梯度微点电极库与选择的成分梯度库相补充，并确定成分空间以进一步阐明速率限制步骤。电化学研究与广泛的物理和化学表征方法相辅相成，以提供与电催化相关的材料行为的全面图景。对电化学反应途径的新见解是创造下一代电化学能量存储和转换装置的重要一步，因此在可持续能源的未来中具有重要作用。

NON-TECHNICAL DESCRIPTION:The goal of this research to advance the fundamental understanding of the behavior of oxide electrodes used in fuel cells, electrolysis cells, batteries, and other energy technologies. The approach combines high-throughput synthesis of libraries of material structures, with advanced high-throughput characterization and high-throughput data analysis. By making use of structures with well-defined geometric features, it is possible to directly interpret the electrochemical data. The insight afforded in turn enables deliberate engineering of structures to achieve exceptional performance. It also provides chemical guidance on how to create next generation materials. The performance enhancements can ultimately advance goals in sustainable energy. A broad cross-section of students at all levels are incorporated into the research and training goals of this effort via internships for high school and undergraduate students, as well as doctoral research opportunities for graduate students. Outreach efforts include engaging local K-12 students in science and engineering.TECHNICAL DESCRIPTION:This work aims to dramatically advance the understanding of electrochemical reaction pathways by making use of geometrically well-defined systems. Typical electrochemical structures incorporate random, high-surface area features to maximize overall performance and are not well-suited to extraction of fundamental behavior. In contrast, geometrically well-defined systems enable determination of properties such as length-specific triple-phase boundary activity, bulk chemical diffusion coefficient, area-specific surface activity, and much more. These are essential parameters for the deliberate engineering of high-performance structures. The painstaking nature of acquiring such data using individually prepared samples has, however, limited the study of geometrically well-defined electrochemical systems to a few important examples, despite growing recognition of its value. In this project, advanced fabrication tools are utilized to create libraries of electrode structures on electrolyte substrates and rapidly measure the entire contents of each library using an in-house constructed, unique scanning electrochemical probe system. Computational tools are developed to handle the massive quantities of data generated, including data mining and machine learning capabilities to create efficiencies in data acquisition and analysis. Libraries of geometrically graded microdot electrodes are complemented with selected compositionally-graded libraries, with the compositional space identified to further elucidate rate-limiting steps. Electrochemical studies are complemented with a broad suite of physical and chemical characterization methods to provide a comprehensive picture of material behavior as relevant to electrocatalysis. Generation of new insights into electrochemical reaction pathways is an essential step in the creation of next-generation electrochemical energy storage and conversion devices and as such has an important role in a sustainable energy future.