High-throughput experiments for synthesis and applications of zeolites

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Introduction

High-throughput experimentation has entered materials science and – with a slight delay – catalysis in the middle of the nineties, when the concepts originally developed in the pharmaceutical industry for drug discovery were adapted and applied in these fields [1], although some of the initial ideas date back to the 1970s with the so-called compositional spread approach [2]. High-throughput experimentation is a concerted approach, comprising several different elements which need to be integrated in the high-throughput workflow. These are (i) the automated, often parallel synthesis of solids, (ii) the parallel or fast sequential testing of the relevant properties of the solids, (iii) data management to integrate all the data and to extract knowledge, and (iv) often a robotics environment. In addition, to some extent high-throughput characterization technology is necessary, if a correlation of physico-chemical parameters of the synthesized solids with the performance is desired.

All scientists active in the field of zeolites know that often wide ranges of parameters and synthesis conditions have to be screened in order to optimize the synthesis of a zeolite or to discover novel materials. The same holds for the evaluation of performance data of zeolites in various applications. Zeolite science and technology is therefore a field where there is a high need to accelerate experimentation and to achieve a higher throughput. Thus, relatively early on in the development of high-throughput experimentation, efforts were made to use the concepts in the zeolite field. The state-of-the-art in this field, possible directions of future work, and the limitation as far as they can be perceived at this stage will be discussed in the following.

Section snippets

Synthesis

Zeolite synthesis is one of the fields in solid state chemistry, where predictability of the outcome of the synthesis is rather low. It is in most cases not clear, why specific structures do form with certain template molecules and under certain synthesis conditions. Some success has been reached in a more rational zeolite design [3], either by computational design of specific templates or by judicious addition of heteroatoms to induce the formation of certain ring sizes in the structures.

Characterization

In the preceding chapter it had already been discussed that it is less the synthesis itself which may be the bottleneck in high-throughput zeolite science but rather the analysis of the solids formed in a high-throughput program. There are several standard characterization techniques which are typically employed to characterize zeolitic materials. These include powder XRD for phase identification, X-ray fluorescence analysis (XRF) or atomic absorption spectrometry to analyze elemental

CAtalytic Testing

One of the most important steps in high-throughput development of catalysts is the catalytic test itself. For catalytic testing, typically stage I (or “discovery”) and stage II (“optimization”) systems are discriminated [28]. In a stage I project, typically little is known about the catalytic system, screening is very broad and massively parallelized, the depth of information is rather low, the reaction conditions are often far away from technical conditions, and often the format of the reactor

Library Design and Data Analysis

The more advanced a high-throughput program in catalysis becomes the more important are intelligent design of the libraries and efficient methods for “knowledge extraction” which are typically computer based. These fields are at present still in their infancy, and applications in zeolite science are scarce. Nevertheless, as these problems are highly relevant in high-throughput experimentation, they should at least briefly be addressed.

When a library of catalysts for a specific problem shall be

Perspectives

The different components of an integrated high-throughput approach for zeolite science are in different stages of development. While synthesis, part of the characterization, and catalytic testing are already well developed and in several laboratories used on a routine basis, software based tools for the design of libraries and the extraction of knowledge from the vast amounts of data are still in their infancy. Depending on the type of the problem to be studied, it can be useful to employ

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