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

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，通过新型的快速实验加速了新型催化材料的发现。这项研究的目标是将FTIR成像作为一种真正的并行、高通量、化学灵敏的分析技术，结合分子水平和反应器规模的建模来深入了解催化剂体系的纳米级行为。建模方法将专门针对接受来自高通量实验的信息而量身定做，在实验和建模之间创造协同效应。这项工作的目的是以并行的方式收集高信息量的数据，以获得关于汽车尾气后处理的NOx储存和还原催化剂以及用于汽车燃料电池的车载氢气的直接氨分解催化剂的科学知识。该项目将培训学生了解这种新方法的概念，以研究、开发新的实验分析技术、实验设计和不同长度尺度的数据建模。

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.