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: 1505103
• 负责人: Sossina Haile
• 金额: $ 47.5万
• 依托单位: Northwestern University
• 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 2022-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1505103&HistoricalAwards=false
• 关键词: Collaborative; Research; Throughput; Quantification; Solid

NON-TECHNICAL DESCRIPTION:The goal of this research is 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：这项研究的目标是推进对燃料电池，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。该方法将材料结构库的高通量合成与先进的高通量表征和高通量数据分析相结合。通过利用具有明确定义的几何特征的结构，可以直接解释电化学数据。反过来，所提供的洞察力使精心设计的结构能够实现卓越的性能。它还提供了如何创建下一代材料的化学指导。性能增强最终可以推进可持续能源的目标。通过高中和本科生的实习以及研究生的博士研究机会，各级学生的广泛横截面被纳入这一努力的研究和培训目标。推广工作包括让当地K-12学生参与科学和工程。技术说明：这项工作的目的是通过利用几何定义良好的系统，大大提高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特征以最大化整体性能，并且不太适合于基本行为的提取。相比之下，几何定义明确的系统能够确定诸如长度特定的三相边界活性、本体化学扩散系数、面积特定的表面活性等特性。这些都是高性能结构精心设计的基本参数。然而，使用单独制备的样品获取此类数据的艰苦性质将几何定义良好的电化学系统的研究限制在少数重要的例子中，尽管人们越来越认识到其价值。在这个项目中，先进的制造工具被用来创建电解质基板上的电极结构库，并使用内部构建的，独特的扫描电化学探针系统快速测量每个库的全部内容。开发计算工具来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。几何分级的微点电极的库与所选的组成分级库互补，其中组成空间被识别以进一步阐明限速步骤。电化学研究与一系列广泛的物理和化学表征方法相结合，以提供与电催化相关的材料行为的全面描述。对电化学反应途径产生新的见解是创建下一代电化学能量存储和转换装置的重要步骤，因此在可持续能源的未来中具有重要作用。

项目成果

Insensitivity of the extent of surface reduction of ceria on termination: comparison of (001), (110), and (111) faces

• DOI: 10.1557/mrc.2020.73
• 发表时间: 2020-12
• 期刊: MRS Communications
• 影响因子: 1.9
• 作者: W. Yuan;S. Haile

Exceptional power density and stability at intermediate temperatures in protonic ceramic fuel cells

• DOI: 10.1038/s41560-017-0085-9
• 发表时间: 2018-03-01
• 期刊: NATURE ENERGY
• 影响因子: 56.7
• 作者: Choi, Sihyuk;Kucharczyk, Chris J.;Haile, Sossina M.
• 通讯作者: Haile, Sossina M.

Unexpected trends in the enhanced Ce 3+ surface concentration in ceria–zirconia catalyst materials

二氧化铈-氧化锆催化剂材料中 Ce 3 表面浓度提高的意外趋势

• DOI: 10.1039/d0ta02762f
• 发表时间: 2020
• 期刊: Journal of Materials Chemistry A
• 影响因子: 11.9
• 作者: Yuan, Weizi;Ma, Qing;Liang, Yangang;Sun, Chengjun;Narayanachari, K. V.;Bedzyk, Michael J.;Takeuchi, Ichiro;Haile, Sossina M.
• 通讯作者: Haile, Sossina M.