Combined high-throughput spectroscopic and modeling approach to the study of heterogeneous catalysts

结合高通量光谱和建模方法来研究多相催化剂

• 批准号: 0343758
• 负责人: Jochen Lauterbach
• 金额: $ 29.13万
• 依托单位: University of Delaware
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-04-15 至 2008-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0343758&HistoricalAwards=false
• 关键词: Combined; throughput; spectroscopic; modeling; approach

In the field of material science, the vast array of possible compositions, structures, and formulations leads to a very high complexity, which has often kept researchers from predicting material behavior based on the formulation and, even more importantly, from designing better materials. The advent of high-throughput technologies has made a large impact in the past few years, accelerating the discovery of novel catalytic materials through new kinds of rapid experimentation. The goal of this research is to employ FTIR imaging as a truly parallel, high throughput, chemically sensitive analytical technique in conjunction with molecular-level and reactor scale modeling to gain insight into the nanoscale behavior of catalyst systems. The modeling approach will specifically be tailored towards accepting information from high throughput experimentation, creating a synergy between experiments and modeling. This work is aimed at the collection of high information content data in a parallel fashion in order to gain scientific knowledge about NOx storage and reduction catalysts for automotive exhaust aftertreatment and direct ammonia decomposition catalysts for onboard generation of hydrogen for mobile fuel cell applications.This program will train students in the concept of this novel approach to research, development of novel experimental analytical techniques, the design of experiments, and data modeling on various length scales.

在材料科学领域，大量可能的成分、结构和配方导致了非常高的复杂性，这往往使研究人员无法根据配方预测材料行为，更重要的是，无法设计出更好的材料。高通量技术的出现在过去几年中产生了巨大的影响，通过新型快速实验加速了新型催化材料的发现。本研究的目标是采用FTIR成像作为一种真正并行的，高通量的，化学敏感的分析技术，结合分子水平和反应器规模的建模，以深入了解催化剂系统的纳米行为。建模方法将专门针对接受来自高通量实验的信息进行定制，从而在实验和建模之间产生协同作用。这项工作的目的是收集高信息含量的数据在一个平行的方式，以获得有关NOx的存储和还原催化剂的汽车尾气后处理和直接氨分解催化剂的车载发电氢气的移动的燃料电池的应用的科学知识。该计划将培养学生在这种新的方法的概念，研究，开发新的实验分析技术，实验的设计，以及各种长度尺度上的数据建模。