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Mass Spectrometry (MS) and Nuclear Magnetic Resonance Spectroscopy (NMR) are critical components of every industrial chemical process as they provide information on the concentrations of individual compounds and by-products. These processes are carried out manually and by a specialist, which takes a substantial amount of time and prevents their utilization for real-time closed-loop process control. This paper presents recent advances from two projects that use Artificial Neural Networks (ANNs) to address the challenges of automation and performance-efficient realizations of MS and NMR. In the first part, a complete toolchain has been developed to develop simulated spectra and train ANNs to identify compounds in MS. In the second part, a limited number of experimental NMR spectra have been augmented by simulated spectra to train an ANN with better prediction performance and speed than state-of-theart analysis. These results suggest that, in the context of the digital transformation of the process industry, we are now on the threshold of a possible strongly simplified use of MS and MRS and the accompanying data evaluation by machine-supported procedures, and can utilize both methods much wider for reaction and process monitoring or quality control.
Short diffusion paths and high specific interfacial areas in microstructured devices can increase mass transfer rates and thus accelerate multiphase reactions. This effect can be intensified by the application of ultrasound. Herein, we report on the design and testing of a novel versatile setup for a continuous ultrasound-supported multiphase process in microstructured devices on a preparative scale. The ultrasonic energy is introduced indirectly into the microstructured device through pressurized water as transfer medium. First, we monitored the influence of ultrasound on the slug flow of a liquid/liquid two-phase system in a channel with a high-speed camera. To quantify the influence of ultrasound, the hydrolysis of p-nitrophenyl acetate was utilized as a model reaction. Microstructured devices with varying channel diameter, shape, and material were applied with and without ultrasonication at flow rates in the mL min-1 range. The continuous procedures were then compared and evaluated by performing a simplified life cycle assessment.
Mass Spectrometry (MS) and Nuclear Magnetic Resonance Spectroscopy (NMR) are critical components of every industrial chemical process as they provide information on the concentrations of individual compounds and by-products. These processes are carried out manually and by a specialist, which takes a substantial amount of time and prevents their utilization for real-time closed-loop process control. This paper presents recent advances from two projects that use Artificial Neural Networks (ANNs) to address the challenges of automation and performance-efficient realizations of MS and NMR. In the first part, a complete toolchain has been developed to develop simulated spectra and train ANNs to identify compounds in MS. In the second part, a limited number of experimental NMR spectra have been augmented by simulated spectra to train an ANN with better prediction performance and speed than state-of-theart analysis. These results suggest that, in the context of the digital transformation of the process industry, we are now on the threshold of a possible strongly simplified use of MS and MRS and the accompanying data evaluation by machine-supported procedures, and can utilize both methods much wider for reaction and process monitoring or quality control.
The absorption and emission properties of organic dyes are generally tuned by altering the substitution pattern. However, tuning the fluorescence lifetimes over a range of several 10 ns while barely affecting the spectral features and maintaining a moderate fluorescence quantum yield is challenging. Such properties are required for lifetime multiplexing and barcoding applications. Here, we show how this can be achieved for the class of fluoranthene dyes, which have substitution-dependent lifetimes between 6 and 33 ns for single wavelength excitation and emission. We explore the substitution-dependent emissive properties in the crystalline solid state that would prevent applications.
Furthermore, by analyzing dye mixtures and embedding the dyes in carboxyfunctionalized 8 μm-sized polystyrene particles, the unprecedented potential of these dyes as labels and encoding fluorophores for time-resolved fluorescence detection techniques is demonstrated.
Mass Spectrometry (MS) and Nuclear Magnetic Resonance Spectroscopy (NMR) are valuable analytical and quality control methods for most industrial chemical processes as they provide information on the concentrations of individual compounds and by-products. These processes are traditionally carried out manually and by a specialist, which takes a substantial amount of time and prevents their utilization for real-time closed-loop process control. This paper presents recent advances from two projects that use Artificial Neural Networks (ANNs) to address the challenges of automation and performance-efficient realizations of MS and NMR. In the first part, a complete toolchain has been realized to develop simulated spectra and train ANNs to identify compounds in MS. In the second part, a limited number of experimental NMR spectra have been augmented by simulated spectra, to train an ANN with better prediction performance and speed than state-of-the-art analysis. These results suggest that, in the context of the digital transformation of the process industry, we are now on the threshold of a strongly simplified use of MS and MRS and the accompanying data evaluation by machine-supported procedures, and can utilize both methods much wider for reaction and process monitoring or quality control.