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An easy, inexpensive, and rapid method to identify microorganisms is in great demand in various areas such as medical diagnostics or in the food industry. In our study, we show the development of several predictive models based on Raman spectroscopy combined with support vector machines (SVM) for 21 species of microorganisms. The microorganisms, grown under standardized conditions, were placed on a silver mirror slide to record the data for model development. Additional data was obtained from microorganisms on a polished stainless-steel slide in order to validate the models in general and to assess possible negative influences of the material change on the predictions. The theoretical prediction accuracies for the most accurate models, based on a five-fold cross-validation, are 98.4%. For practical validation, new spectra (from stainless-steel surfaces) have been used, which were not included in the calibration data set. The overall prediction accuracy in practice was about 80% and the inaccurate predictions were only due to a few species. The development of a database provides the basis for further investigations such as the application and extension to single-cell analytics and for the characterization of biofilms.

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The catalytically coated ceramic plate heat exchanger represents a specialized microreactor offering a large surface‐to‐volume ratio and improved temperature control. Appropriate and economically viable manufacturing processes are presented together with the results of coating studies and thermal and reaction engineering investigations. The selective methanation of carbon monoxide, used as purification step for H2‐rich gas streams in stationary fuel cell applications, was chosen as a suitable test reaction because of its strongly exothermic behavior and its need for tight temperature control.