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

合作研究：下一代能源技术的固态电化学高通量定量

• 批准号: 1505116
• 负责人: Ichiro Takeuchi
• 金额: $ 45.5万
• 依托单位: University of Maryland, College Park
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-07-01 至 2019-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1505116&HistoricalAwards=false
• 关键词: Collaborative; Research; Throughput; Quantification; Solid

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.

非技术描述：这项研究的目的是促进对燃料电池、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了解。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。所提供的洞察力反过来又使精心设计的结构能够实现卓越的性能。它还提供了关于如何创造下一代材料的化学指导。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，将各级广泛的学生纳入这一努力的研究和培训目标。技术描述：这项工作的目的是通过使用几何定义明确的系统，极大地促进对电化学反应途径的理解。典型的电化学结构结合了随机的、高表面积的特征来最大化整体性能，并且不太适合基本行为的提取。相比之下，几何定义良好的体系可以确定诸如特定长度的三相边界活性、整体化学扩散系数、特定区域的表面活性等性质。这些都是精心设计高性能结构的基本参数。然而，使用单独制备的样品获取此类数据的艰苦性质限制了对几何定义明确的电化学体系的研究仅限于几个重要的例子，尽管人们越来越认识到它的价值。在该项目中，利用先进的制造工具在电解液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。开发了计算工具来处理产生的海量数据，包括数据挖掘和机器学习能力，以提高数据获取和分析的效率。几何梯度微点电极库与选定的成分梯度库互补，识别组成空间以进一步阐明限速步骤。电化学研究辅以一套广泛的物理和化学表征方法，以提供与电催化相关的材料行为的全面图景。对电化学反应途径产生新的见解是创造下一代电化学能量存储和转换装置的关键步骤，因此在可持续能源的未来中具有重要作用。