TY - JOUR A1 - Fricke, F. A1 - Mahmood, S. A1 - Hoffmann, J. A1 - Brandalero, M. A1 - Liehr, Sascha A1 - Kern, Simon A1 - Meyer, Klas A1 - Kowarik, S. A1 - Westerdick, S. A1 - Maiwald, Michael A1 - Hübner, M. T1 - Artificial Intelligence for Mass Spectrometry and Nuclear Magnetic Resonance Spectroscopy N2 - 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. T2 - 2021 Design, Automation & Test in Europe Conference & Exhibition (DATE) CY - Grenoble, France DA - 01.02.2021 KW - Industry 4.0, KW - Cyber-physical systems KW - Artificial neural networks KW - Mass spectrometry KW - Nuclear magnetic resonance spectroscopy PY - 2021 DO - https://doi.org/10.23919/DATE51398.2021.9473958 SP - 615 EP - 620 PB - IEEE AN - OPUS4-55360 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Hübner, S. A1 - Kressirer, S. A1 - Kralisch, D. A1 - Bludszuweit-Philipp, C. A1 - Lukow, K. A1 - Jänich, I. A1 - Schilling, A. A1 - Hieronymus, Hartmut A1 - Liebner, Christian A1 - Jähnisch, K. T1 - Ultrasound and microstructures - a promising combination? N2 - 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. KW - Biphasic reactions KW - Hydrolysis KW - Interfaces KW - Liquids KW - Ultrasound PY - 2012 DO - https://doi.org/10.1002/cssc.201100369 SN - 1864-5631 SN - 1864-564X VL - 5 IS - 2 SP - 279 EP - 288 PB - Wiley-VCH CY - Weinheim AN - OPUS4-25476 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Fricke, F. A1 - Mahmood, S. A1 - Hoffmann, J. A1 - Brandalero, M. A1 - Liehr, Sascha A1 - Kern, Simon A1 - Meyer, Klas A1 - Kowarik, Stefan A1 - Westerdicky, S. A1 - Maiwald, Michael A1 - Hübner, M. T1 - Artificial Intelligence for Mass Spectrometry and Nuclear Magnetic Resonance Spectroscopy N2 - 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. T2 - 2021 Design, Automation & Test in Europe Conference & Exhibition (DATE) CY - Online meeting DA - 01.02.2021 KW - Industry 4.0 KW - Cyber-Physical Systems KW - Artificial Neural Networks KW - Mass Spectrometry KW - Nuclear Magnetic Resonance Spectroscopy PY - 2021 UR - www.date-conference.com SN - 978-3-9819263-5-4 SP - 615 EP - 620 PB - Research Publishing AN - OPUS4-52180 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - Pinto, H. A1 - Stuke, S. A1 - Pyzalla, A. A1 - Hübner, Wolfgang A1 - Aßmus, Kristin T1 - Vergleich der Verformungsmechanismen beim Tieftemperaturverschleiß der austenitischen Stähle 1.4301 und 1.4439 T2 - 44. Tribologie-Fachtagung CY - Göttingen, Deutschland DA - 2003-09-22 PY - 2003 SN - 3-00-003404-8 VL - 1 SP - 7-1-7-10 PB - GfT CY - Moers AN - OPUS4-3148 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Hübner, Wolfgang A1 - Gradt, Thomas A1 - Aßmus, Kristin A1 - Pyzalla, A. A1 - Pinto, H. A1 - Stuke, S. T1 - Hydrogen absorption by steels during friction T2 - Hydrogen and Fuel Cells Conference and Trade Show ; Hydrogène et Piles à Combustible Conférence et Foire Commerciale CY - Vancouver, Canada DA - 2003-06-08 PY - 2003 SP - 1 EP - 11(?) PB - CHA CY - Toronto AN - OPUS4-3319 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Procop, Mathias A1 - Hübner, Wolfgang A1 - Wäsche, Rolf A1 - Nieland, S. A1 - Ehrmann, O. T1 - Fast Elemental Mapping in Materials Science KW - Electron probe microanalysis KW - EPMA KW - Energy dispersive X-ray spectroscopy KW - EDS KW - Elemental mapping PY - 2002 UR - http://www.microscopy-analysis.com/sites/default/files/magazine_pdfs/mag%20179_2002_Jan_Procop_1.pdf SN - 0958-1952 IS - January SP - 5 EP - 6 PB - Rolston Gordon Comm. CY - Bookham, Surrey AN - OPUS4-7091 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Hübner, S. A1 - Jähnisch, K. A1 - Bludszuweit-Phillip, C. A1 - Lukow, K. A1 - Schilling, A. A1 - Huebschmann, S. A1 - Krailisch, D. A1 - Jänich, I. A1 - Liebner, Christian A1 - Hieronymus, Hartmut T1 - The development of ultrasound supported multiphase reactions in microstructured devices T2 - ProcessNet Jahrestagung 2010 CY - Aachen, Germany DA - 2010-09-21 PY - 2010 AN - OPUS4-22135 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Rietsch, P. A1 - Zeyat, M. A1 - Hübner, Oskar A1 - Hoffmann, Katrin A1 - Resch-Genger, Ute A1 - Kutter, M. A1 - Paskin, A. A1 - Uhlig, J. A1 - Lentz, D. A1 - Eigler, S. T1 - Substitution Pattern-Controlled Fluorescence Lifetimes of Fluoranthene Dyes N2 - 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. KW - Fluorescence KW - Label KW - Fluoranthene KW - Quantum yield KW - Reporter KW - Crystal KW - Encoding KW - Multiplexing KW - Particle KW - Bead KW - Lifetime KW - Dye KW - Barcoding PY - 2021 DO - https://doi.org/10.1021/acs.jpcb.0c08851 SN - 1520-5207 VL - 125 IS - 4 SP - 1207 EP - 1213 PB - American Chemical Society AN - OPUS4-52087 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Fricke, F. A1 - Brandalero, M. A1 - Liehr, Sascha A1 - Kern, Simon A1 - Meyer, Klas A1 - Kowarik, Stefan A1 - Hierzegger, R. A1 - Westerdick, S. A1 - Maiwald, Michael A1 - Hübner, M. T1 - Artificial Intelligence for Mass Spectrometry and Nuclear Magnetic Resonance Spectroscopy Using a Novel Data Augmentation Method N2 - 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. KW - Industry 4.0 KW - Cyber-Physical Systems KW - Artificial Neural Networks KW - Mass Spectrometry KW - Nuclear Magnetic Resonance Spectroscopy KW - Modular Production PY - 2021 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-539412 UR - https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9638378 DO - https://doi.org/10.1109/TETC.2021.3131371 SN - 2168-6750 VL - 10 IS - 1 SP - 87 EP - 98 PB - IEEE AN - OPUS4-53941 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -