Spectroscopic Imaging in the Mid-Infrared Applied to High-Throughput Studies of Supported Catalyst Libraries

  • Steven S. Lasko
  • Reed J. Hendershot
  • Yu Fu
  • Mark-Florian Fellmann
  • Gudbjorg Oskarsdottir
  • Christopher M. Snively
  • Jochen Lauterbach

Abstract

The “combinatorial approach” has shown very promising results for pharmaceuticals and small organic molecules [1, 2, 3]. High-throughput (HT) screening of heterogeneous catalysts goes back at least IS years [4], but has recently been rediscovered as a method for rapidly and efficiently identifying catalyst formulations [5, 6, 7, 8, 9, 10, 11, 12, 13, 14]. The approach consists of two key steps: the systematic synthesis of a large number of potentially useful formulations (collectively referred to herein as a “library,” although this term has recently been contested [15), and the subsequent rapid testing of this library to determine the usefulness of each formulation to the specific application. Several methodologies have been developed for the rapid and efficient generation of catalyst libraries, ranging from evaporation methods [16] and robotic dispensing of catalyst precursors [17–19] to parallel hydrothermal processing [20, 21, 22]. Once generated, the libraries must be characterized and tested. For heterogeneously catalyzed reactions, this step can range from simple qualitative activity screening to the quantitative measurement of selectivity or turnover rates. Currently available analytical techniques are often incapable of keeping pace with the large numbers of compounds created. This creates a bottleneck, slowing the entire discovery process, and has created a serious demand for the development of analytical techniques specifically designed for the HT analysis of combinatorial heterogeneous catalyst libraries.

Keywords

Spectroscopic Image Mercury Cadmium Telluride Indium Antimonide FTIR Imaging Thermal Cross Talk 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media New York 2003

Authors and Affiliations

  • Steven S. Lasko
    • 1
  • Reed J. Hendershot
    • 1
  • Yu Fu
    • 1
  • Mark-Florian Fellmann
    • 1
  • Gudbjorg Oskarsdottir
    • 1
  • Christopher M. Snively
    • 1
  • Jochen Lauterbach
    • 1
  1. 1.Department of Chemical EngineeringUniversity of DelawareNewarkUSA

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