Principal Workflow

In the last few years, this technology has been extended to materials science for the rapid discovery and optimization of, e. g., polymers, catalysts, and electronic materials [1]. Homogeneous catalysis, where catalyst performance can be influenced through choices in ligand, metal, and reaction conditions, represents a very important subset of this broad field. 

The primary screen - up to thousands of reactions per day typically on a microliter scale - provides information about potential hits and serves as a tool to rule out quickly catalyst formulations that perform poorly. The primary screening filter should be suitably high-throughput to cover a selected parameter space rapidly, although to achieve this it is often necessary to tolerate certain compromises regarding the precision of data from such a screen. 

1 Preparation of Homogeneous Catalysts in a High-Throughput Format 

It is very important to stress that the large parameter space in homogeneous catalysis is not represented by the number of metal-ligand combinations alone. To discover a cost-effective scalable process for many transformations, many other parameters have to be considered and optimized. For example, consider the choice of the following important reaction parameters for a given transformation four different ligand/metal ratios, four different pressures, three different substrate/metal ratios, three different solvents, and two different temperatures. The inclusion of these parameters into a HT screen will result in almost 300 experiments for each metal-ligand combination  

2 Screening of Homogeneous Catalysts Against a Chemical Transformation 

---

We implemented the maximum cardinality feature as an extension to the open source program Efmtool [25]. The software is freely available at the author s webpage [26]. The principal workflow of the algorithm is illustrated in Figure 3.2. This extended version was used throughout the remainder of the study and compared... 

All of the metabolomics experiments tend to follow a similar workflow (Figure 8.2), which includes different steps to reach a final interpretation for an initial biological qnestion. Principally, three different parts can be distinguished sample preparation, sample analysis, and data treatment. 

Fig. 22.2. PCA and hierarchical clustering for a gastric cancer section, (a) H E-stained tissue section after MALDI imaging measurement, (b) Scores of the first principal component show the hot colors in the tumor area, (c) Hierarchical clustering Top dendrogram nodes differentiate tumor (green and magenta) versus non-tumor (blue, squamous epitheiium in red), (d) The dendrogram can be expanded down the tumor node to evaluate the moiecuiar differentiation inside the tumor. This can also be directly correlated with the histoiogy. This workflow enables the fast and concise selection of mass spectra representative for specific tissue states. Scale bar 2 mm.

---