DMREF/Collaborative Research: Design and Testing of Nanoalloy Catalysts in 3D Atomic Resolution

DMREF/合作研究：3D 原子分辨率纳米合金催化剂的设计和测试

• 批准号: 1437263
• 负责人: Jianwei Miao
• 金额: $ 80万
• 依托单位: University of California-Los Angeles
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-10-01 至 2020-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1437263&HistoricalAwards=false
• 关键词: DMREF; Collaborative; Research; Design; Testing

DMREF: Collaborative Research: Design and Testing of Nanoalloy Catalysts in 3D Atomic ResolutionNon-Technical Description: The project aims at the discovery of ultra-small, nanometer-sized alloy catalysts to improve the efficiency of fuel cells, mobile power generation, and automotive catalytic converters. State-of-the-art laboratory and computer simulation techniques will be engaged to explore the uncharted design space of bimetallic nanostructures for such applications and implement reductions in cost through partial replacement of precious metals such as platinum by cheaper alternatives. The team of three PIs will synthesize new nanocatalysts using biomimetic approaches, image the positions of all atoms in 3D resolution using the world's most powerful electron microscope, and carry out performance tests in fuel cells in a close feedback loop with predictions by multi-scale modeling and simulation. Fundamental understanding of synthesis controls, atomic-scale order, and associated reactivity of the nanoalloys will lead to rational design rules to optimize catalyst performance and enable targeted improvements of promising materials. The development and validation of predictive multi-scale simulation tools will also benefit the broader computational user community. New fundamental insight into alloy synthesis and reactivity controls has further potential benefits to improve catalysts for commodity chemicals, magnetic information storage, batteries, sensors, and nanoelectronic devices. Undergraduate students, high school students, and teachers will be engaged in summer research activities at UCLA and in annual Engineering Career Days at the University of Akron to encourage careers in science and engineering.Technical Description: The project focuses on the computationally driven, rational optimization of nanoalloy atomic composition and shape for catalytic performance in the Oxygen Reduction Reaction (ORR) in fuel cells. Specific aims include the deterministic synthesis of Pt-M Nanocrystals (M = Fe, Co, Ni, Cu, Cr, Mn), the three-dimensional characterization of nanoalloy catalysts in atomic resolution and model refinements, as well as the prediction, tests, and optimization of the reactivity in the ORR. Methods comprise biomimetic synthesis protocols coupled with molecular dynamics and kinetic Monte Carlo simulations, ORR performance testing by voltammetry and density functional theory calculations, and in-situ monitoring of all reactions. The coordinates of the atoms in the synthesized nanostructures will be monitored by electron tomography to identify atomic ordering, to validate and improve interatomic potentials, and to predict reaction rates in ORR. Detailed understanding of alloy growth mechanism, shape control, and catalytic performance through new polarizable and reactive force fields for alloys and their aqueous interfaces from first principles will close a wide gap between experimental capabilities and missing theoretical understanding. Aided by thorough experimental characterization, predictions with unprecedented accuracy at length scales of 1 to 100 nm appear feasible, far beyond the limits of quantum-mechanical methods and building on previous successful models for interfaces of pure metals (CHARMM-METAL).

DMREF：合作研究：三维原子分辨率纳米合金催化剂的设计和测试非技术描述：该项目旨在发现超小、纳米尺寸的合金催化剂，以提高燃料电池、移动发电和汽车催化转换器的效率。最先进的实验室和计算机模拟技术将用于探索双金属纳米结构的未知设计空间，并通过使用更便宜的替代品部分替代贵金属（如铂）来降低成本。由三位pi组成的团队将使用仿生方法合成新的纳米催化剂，使用世界上最强大的电子显微镜在3D分辨率下对所有原子的位置进行成像，并通过多尺度建模和模拟进行预测，在紧密反馈回路中对燃料电池进行性能测试。对纳米合金的合成控制、原子尺度顺序和相关反应性的基本理解将导致合理的设计规则，以优化催化剂性能，并使有前途的材料有针对性地改进。预测多尺度模拟工具的开发和验证也将使更广泛的计算用户群体受益。对合金合成和反应性控制的新基本见解对改善商品化学品、磁信息存储、电池、传感器和纳米电子器件的催化剂有进一步的潜在好处。本科生、高中生和教师将参与加州大学洛杉矶分校的夏季研究活动，以及阿克伦大学的年度工程职业日，以鼓励科学和工程方面的职业发展。技术描述：该项目专注于计算驱动的纳米合金原子组成和形状的合理优化，用于燃料电池中氧还原反应（ORR）的催化性能。具体目标包括Pt-M纳米晶体（M = Fe, Co, Ni, Cu, Cr, Mn）的确定性合成，纳米合金催化剂在原子分辨率和模型细化方面的三维表征，以及ORR中反应性的预测、测试和优化。方法包括结合分子动力学和动力学蒙特卡罗模拟的仿生合成方案，通过伏安法和密度泛函数理论计算进行ORR性能测试，以及所有反应的现场监测。合成的纳米结构中的原子坐标将通过电子断层扫描来识别原子有序，验证和改进原子间电位，并预测ORR中的反应速率。从第一性原理出发，通过新的极化力场和反应力场对合金及其水界面进行合金生长机制、形状控制和催化性能的详细了解，将缩小实验能力与理论认识之间的巨大差距。在彻底的实验表征的帮助下，在1到100纳米的长度尺度上具有前所未有的精度的预测似乎是可行的，远远超出了量子力学方法的限制，并建立在先前成功的纯金属界面模型（CHARMM-METAL）上。