TY - CONF A1 - Kowarik, Stefan A1 - Karapanagiotis, Christos A1 - Liehr, Sascha T1 - Artificial intelligence, the end of the world, and surface science N2 - Artificial intelligence (AI), machine learning, and neural networks have revolutionized fields such as self-driving cars or machine translation. Indeed, AI has progressed so far that scientists such as Stephen Hawking now fear that it may destroy humankind altogether. So, let’s get started and use AI in surface science. Here we train neural networks to use raw measurement data as input and immediately return the desired output parameters. We discuss the example of X-ray reflectivity measurements of ultrathin films, which contain reciprocal space information and must traditionally be fitted with dynamic scattering theory. Instead, we train the neural network with simulated X-ray data of multilayer structures and then apply it to measurement data. The neural network yields high accuracy results, is robust against noise, and performs significantly faster than fitting algorithms in real-time experiments. The presented neural network data analysis is becoming increasingly attractive, because free software has become very accessible and specialized computer chips (NPU) are currently being rolled out. T2 - DPG Früjahrstagung CY - Berlin, Germany DA - 12.03.2018 KW - X-ray reflectivity KW - AI KW - Machine learning KW - Neural network PY - 2017 AN - OPUS4-44602 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Kowarik, Stefan A1 - Bogula, A1 - Boitano, A1 - Carla, A1 - Pithan, A1 - Schafer, A1 - Wilming, A1 - Zykov, A1 - Pithan, T1 - A novel 3D printed radial collimator for x-ray diffraction N2 - We demonstrate the use of a 3D printed radial collimator in X-ray powder diffraction and surface sensitive grazing incidence X-ray diffraction. We find a significant improvement in the overall Signal to background ratio of up to 100 and a suppression of more than a factor 3⋅10⁵ for undesirable Bragg reflections generated by the X-ray “transparent” windows of the sample environment. The background reduction and the removal of the high intensity signals from the windows, which limit the detector’s dynamic range, enable significantly higher sensitivity in experiments within sample environments such as vacuum chambers and gas- or liquid-cells. Details of the additively manufactured steel collimator geometry, alignment strategies using X-ray fluorescence, and data analysis are also briefly discussed. The flexibility and affordability of 3D prints enable designs optimized for specific detectors and sample environments, without compromising the degrees of freedom of the diffractometer. KW - 3D printing PY - 2019 U6 - https://doi.org/10.1063/1.5063520 SN - 0034-6748 VL - 90 IS - 3 SP - 035102, 1 EP - 8 PB - AIP AN - OPUS4-48171 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Kowarik, Stefan A1 - Hussels, Maria-Teresa A1 - Chruscicki, Sebastian A1 - Münzenberger, Sven A1 - Lämmerhirt, A. A1 - Pohl, P. A1 - Schubert, M. T1 - Fiber Optic Train Monitoring with Distributed Acoustic Sensing: Conventional and Neural Network Data Analysis N2 - Distributed acoustic sensing (DAS) over tens of kilometers of fiber optic cables is well-suited for monitoring extended railway infrastructures. As DAS produces large, noisy datasets, it is important to optimize algorithms for precise tracking of train position, speed, and the number of train cars, The purpose of this study is to compare different data analysis strategies and the resulting parameter uncertainties. We present data of an ICE 4 train of the Deutsche Bahn AG, which was recorded with a commercial DAS system. We localize the train signal in the data either along the temporal or spatial direction, and a similar velocity standard deviation of less than 5 km/h for a train moving at 160 km/h is found for both analysis methods, The data can be further enhanced by peak finding as well as faster and more flexible neural network algorithms. Then, individual noise peaks due to bogie clusters become visible and individual train cars can be counted. From the time between bogie signals, the velocity can also be determined with a lower standard deviation of 0.8 km/h, The analysis methods presented here will help to establish routines for near real-time Train tracking and train integrity analysis. KW - Artificial neural networks KW - Distributed fiber optic sensing KW - Distributed acoustic sensing KW - Train tracking PY - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:b43-502872 VL - 20 IS - 2 SP - 450 PB - MDPI CY - Basel, Switzerland AN - OPUS4-50287 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Kowarik, Stefan A1 - Hicke, Konstantin A1 - Chruscicki, Sebastian A1 - Schukar, Marcus A1 - Breithaupt, Mathias A1 - Lämmerhirt, A. A1 - Pohl, P. A1 - Schubert, M. ED - Cranch, G. ED - Wang, A. ED - Digonnet, M. ED - Dragic, P. T1 - Train monitoring using distributed fiber optic acoustic sensing N2 - We use distributed acoustic sensing to determine the velocity of trains from train vibration patterns using artificial neural network and conventional algorithms. The velocity uncertainty depends on track conditions, train type and velocity. T2 - 27th International Conference on Optical Fiber Sensors (OFS) CY - Meeting was canceled DA - 08.06.2020 KW - Train monitoring KW - Distributed acoustic sensing KW - DAS KW - Distributed fiber optic sensing PY - 2020 SN - 978-1-55752-307-5 U6 - https://doi.org/10.1364/OFS.2020.T3.25 SP - 1 EP - 4 PB - The Optical Society (Optical Society of America) CY - Washington D.C., USA AN - OPUS4-50758 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - Kowarik, Stefan A1 - Pithan, L. T1 - kowarik-labs/AI-reflectivity: v0.1 N2 - AI-reflectivity is a code based on artificial neural networks trained with simulated reflectivity data that quickly predicts film parameters from experimental X-ray reflectivity curves. This project has a common root with (ML-reflectivity)[https://github.com/schreiber-lab/ML-reflectivity] and evolved in parallel. Both are linked to the following publication: Fast Fitting of Reflectivity Data of Growing Thin Films Using Neural Networks A. Greco, V. Starostin, C. Karapanagiotis, A. Hinderhofer, A. Gerlach, L. Pithan, S. Liehr, F. Schreiber, S. Kowarik (2019). J. Appl. Cryst. For an online live demonstration using a pre-trained network have a look at github. KW - Artificial neural networks KW - X-ray reflectivity PY - 2019 U6 - https://doi.org/10.5281/zenodo.3477583 PB - Zenodo CY - Geneva AN - OPUS4-51888 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Greco, A. A1 - Starostin, V. A1 - Karapanagiotis, C. A1 - Hinderhofer, A. A1 - Gerlach, A. A1 - Pithan, L. A1 - Liehr, Sascha A1 - Schreiber, Frank A1 - Kowarik, Stefan T1 - Fast fitting of reflectivity data of growing thin films using neural networks N2 - X-ray reflectivity (XRR) is a powerful and popular scattering technique that can give valuable insight into the growth behavior of thin films. This study Shows how a simple artificial neural network model can be used to determine the thickness, roughness and density of thin films of different organic semiconductors [diindenoperylene, copper(II) phthalocyanine and alpha-sexithiophene] on silica from their XRR data with millisecond computation time and with minimal user input or a priori knowledge. For a large experimental data set of 372 XRR curves, it is shown that a simple fully connected model can provide good results with a mean absolute percentage error of 8–18% when compared with the results obtained by a genetic least mean squares fit using the classical Parratt formalism. Furthermore, current drawbacks and prospects for improvement are discussed. KW - Artificial neural networks KW - X-ray reflectivity PY - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:b43-498997 SN - 1600-5767 VL - 52 SP - 1342 EP - 1347 PB - Wiley AN - OPUS4-49899 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Kockert, M. A1 - Mitdank, R. A1 - Zykov, A. A1 - Kowarik, Stefan A1 - Fischer, F. T1 - Absolute Seebeck coefficient of thin platinum films N2 - The influence of size effects on the thermoelectric properties of thin platinum films is investigated and compared to the bulk. Structural properties, like the film thickness and the grain size, are varied. We correlate the electron mean free path with the temperature dependence of the electrical conductivity and the absolute Seebeck coefficient SPt of platinum. A measurement platform was developed as a standardized method to determine SPt and show that SPt,film is reduced compared to SPt,bulk. Boundary and surface scattering reduce the thermodiffusion and the phonon drag contribution to SPt,film by nearly the same factor. We discuss in detail on behalf of a model, which describes the temperature dependence of the absolute Seebeck coefficient, the influence of size effects of electron-phonon and phonon-phonon interaction on SPt. KW - Thin magnetic films PY - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:b43-499007 SN - 0021-8979 VL - 126 SP - 105106 PB - AIP AN - OPUS4-49900 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Kowarik, Stefan A1 - Krebber, Katerina T1 - Smart materials and structures based on fiber optic sensing N2 - This talk gives an overview of basic techniques. Towards the end some applications of neural networks are discussed. T2 - BAM - IFW workshop CY - Berlin, Germany DA - 25.11.2019 KW - Artificial neural networks KW - Fiber optic sensing PY - 2019 AN - OPUS4-49901 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Kowarik, Stefan A1 - Krebber, Katerina T1 - Neue Entwicklungen der Faseroptische Sensorik: von Infrastruktur-Monitoring zu neuronalen Netzen N2 - Der Vortrag gibt eine Einführung zu obigen Themen auf Niveau von Master Studierenden. T2 - TUM-Tag CY - BAM, Berlin, Adlershof DA - 19.06.2019 KW - Faseroptische Sensorik PY - 2019 AN - OPUS4-49902 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Kowarik, Stefan T1 - Fiber Optic sensing @BAM N2 - I will discuss fiber optic sensing principles at BAM. Overlapping areas of interes between our group and the Institut für Angewandte Photonik will be discussed. T2 - Eingeladener Vortrag am Institut für Angewandte Photonik Adlershof CY - Adlershof, Berlin, Germany DA - 19.11.2017 KW - Faseroptische Sensorik KW - Fiber optic sensing KW - X-ray optics PY - 2017 AN - OPUS4-43587 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Kowarik, Stefan A1 - Schukar, Marcus A1 - Wischerhoff, E. A1 - Krebber, Katerina T1 - Neuartiger faseroptischer Temperatursensor basierend auf thermoresponsiven Polymeren N2 - Wir zeigen Resultate für einen faseroptischen Temperatursensor, der auf der temperaturabhängigen Eintrübung einer wässrigen Polymerlösung beruht. Da der Sensor auf dem Phasenübergang der spinodalen Entmischung bei fester Temepratur beruht, kann sich der Sensor selbst kalibrieren und daher für Anwendungen zur absoluten Temperaturmessung eingesetzt werden. T2 - Hybrid Sensor Net CY - Karlsruhe, Germany DA - 22.11.2017 KW - LCST PY - 2017 AN - OPUS4-43310 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Kowarik, Stefan T1 - Triggering molecular scale processes with light: from photoalignment of molecules to amplification in molecular switches N2 - We present recent results on photoalignment and cooperative molecular switching in thin films and nanofibers. T2 - Seminar am Institut für biomedizinische Optik der LMU München CY - München, Germany DA - 30.11.2017 KW - Molecular switches PY - 2017 AN - OPUS4-43312 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Liehr, Sascha A1 - Jäger, L. A. A1 - Karapanagiotis, Christos A1 - Münzenberger, Sven A1 - Kowarik, Stefan T1 - Real-time dynamic strain sensing in optical fibers using artificial neural networks N2 - We propose to use artificial neural networks (ANNs) for raw measurement data interpolation and signal shift computation and to demonstrate advantages for wavelength-scanning coherent optical time domain reflectometry (WS-COTDR) and dynamic strain distribution measurement along optical fibers. The ANNs are trained with synthetic data to predict signal shifts from wavelength scans. Domain adaptation to measurement data is achieved, and standard correlation algorithms are outperformed. First and foremost, the ANN reduces the data analysis time by more than two orders of magnitude, making it possible for the first time to predict strain in real-time applications using the WS-COTDR approach. Further, strain noise and linearity of the sensor response are improved, resulting in more accurate measurements. ANNs also perform better for low signal-to-noise measurement data, for a reduced length of correlation input (i.e., extended distance range), and for coarser sampling settings (i.e., extended strain scanning range). The general applicability is demonstrated for distributed measurement of ground movement along a dark fiber in a telecom cable. The presented ANN-based techniques can be employed to improve the performance of a wide range of correlation or interpolation problems in fiber sensing data analysis and beyond. KW - Distributed vibration sensing KW - Distributed acoustic sensing KW - Coherent optical time domain reflectometry KW - Optical fiber sensor KW - Artificial neural network KW - Real time measurement KW - Distributed strain sensing PY - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:b43-474832 UR - https://www.osapublishing.org/oe/abstract.cfm?uri=oe-27-5-7405 SN - 1094-4087 VL - 27 IS - 5 SP - 7405 EP - 7425 PB - Optical Society of America AN - OPUS4-47483 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Kowarik, Stefan A1 - Weber, C. A1 - Pithan, L. A1 - Zykov, A. A1 - Bommel, S. A1 - Carla, F. A1 - Felici, R. A1 - Knie, C. A1 - Bléger, D. T1 - Multiple timescales in the photoswitching kinetics of crystalline thin films of azobenzene-trimers N2 - Functional materials that exhibit photoinduced structural phase transitions are highly interesting for applications in optomechanics and mechanochemistry. It is, however, still not fully understood how photochemical reactions, which are often accompanied by molecular motion, proceed in confined and crystalline environments. Here we show that thin films of azobenzene trimers exhibit high structural order and determine the crystallographic unit cell. We demonstrate that thin film can be switched partially reversibly between a crystalline and an amorphous phase. The time constant of the photoinduced amorphisation as measured with real-time x-ray diffraction ($\approx $ 220 s) lies between the two time constants (120 s and 2870 s) of the ensemble photoisomerisation processes that are measured via optical spectroscopy. Our observation of a photoinduced shrinking of the crystalline domains indicates a cascading process, in which photoisomerisation starts at the surface of the thin film and propagates deeper into the crystalline layer by introducing disorder and generating free volume. This finding is important for the rapidly evolving research field of photoresponsive thin films and smart crystalline materials in general. KW - Azobenzene PY - 2017 U6 - https://doi.org/10.1088/1361-648X/aa8654 SN - 0953-8984 SN - 1361-648X VL - 29 IS - 43 SP - Article 434001, 1 EP - 8 AN - OPUS4-42501 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Kowarik, Stefan A1 - Pithan, L. A1 - Nabok, D. A1 - Cocchi, C. A1 - Beyer, P. A1 - Duva, G. A1 - Simbrunner, J. A1 - Rawle, J. A1 - Nicklin, C. A1 - Schäfer, P. A1 - Draxl, C. A1 - Schreiber, F. T1 - Molecular structure of the substrate-induced thin-film phase of tetracene N2 - We present a combined experimental and theoretical study to solve the unit-cell and molecular arrangement of the tetracene thin film (TF) phase. TF phases, also known as substrate induced phases (SIP), are polymorphs that exist at interfaces and decisively impact the functionality of organic thin films, e.g., in a transistor channel, but also change the optical spectra due to the different molecular packing. As SIPs only exist in textured ultrathin films, their structure determination remains challenging compared to bulk materials. Here, we use grazing incidence Xray diffraction and atomistic simulations to extract the TF unit-cell parameters of tetracene together with the atomic positions within the unit-cell. KW - X-ray DIFFRACTION KW - Tetracene KW - Semiconductor KW - Optoelectronics KW - Molecule PY - 2018 U6 - https://doi.org/10.1063/1.5043379 VL - 149 IS - 14 SP - 144701-1 EP - 144701-5 PB - AIP Publishing AN - OPUS4-46399 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Duva, G. A1 - Pithan, L. A1 - Zeiser, C. A1 - Reisz, B. A1 - Dieterle, J. A1 - Hofferberth, B. A1 - Beyer, P. A1 - Bogula, L. A1 - Opitz, A. A1 - Kowarik, Stefan A1 - Hinderhofer, A. A1 - Gerlach, A. A1 - Schreiber, F. T1 - Thin-Film Texture and Optical Properties of Donor/Acceptor Complexes. Diindenoperylene/F6TCNNQ vs Alpha-Sexithiophene/ F6TCNNQ N2 - In this work, two novel donor/acceptor (D/A) complexes, namely, diindenoperylene (DIP)/1,3,4,5,7,8-hexafluoro-tetracyanonaphthoquinodimethane (F6TCNNQ) and alpha-sexithiophene (6T)/F6TCNNQ, are studied. The D/A complexes segregate in form of π−π stacked D/A cocrystals and can be observed by X-ray scattering. The different conformational degrees of freedom of the donor molecules, respectively, seem to affect the thin-film crystalline texture and composition of the D/A mixtures significantly. In equimolar mixtures, for DIP/F6TCNNQ, the crystallites are mostly uniaxially oriented and homogeneous, whereas for 6T/F6TCNNQ, a mostly 3D (isotropic) orientation of the crystallites and coexistence of domains of pristine compounds and D/A complex, respectively, are observed. Using optical absorption spectroscopy, we observe for each of the two mixed systems a set of new, strong transitions located in the near-IR range below the gap of the pristine compounds: such transitions are related to charge-transfer (CT) interactions between donor and acceptor. The optical anisotropy of domains of the D/A complexes with associated new electronic states is studied by ellipsometry. We infer that the CT-related transition dipole moment is perpendicular to the respective π-conjugated planes in the D/A complex. KW - Optical properties KW - Molecular semiconductor KW - X-ray diffraction PY - 2018 U6 - https://doi.org/10.1021/acs.jpcc.8b03744 SP - 18705 EP - 18714 PB - ACS AN - OPUS4-46400 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 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:b43-539412 UR - https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9638378 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 - TY - JOUR A1 - Kern, Simon A1 - Liehr, Sascha A1 - Wander, Lukas A1 - Bornemann-Pfeiffer, Martin A1 - Müller, S. A1 - Maiwald, Michael A1 - Kowarik, Stefan T1 - Artificial neural networks for quantitative online NMR spectroscopy N2 - Industry 4.0 is all about interconnectivity, sensor-enhanced process control, and data-driven systems. Process analytical technology (PAT) such as online nuclear magnetic resonance (NMR) spectroscopy is gaining in importance, as it increasingly contributes to automation and digitalization in production. In many cases up to now, however, a classical evaluation of process data and their transformation into knowledge is not possible or not economical due to the insufficiently large datasets available. When developing an automated method applicable in process control, sometimes only the basic data of a limited number of batch tests from typical product and process development campaigns are available. However, these datasets are not large enough for training machine-supported procedures. In this work, to overcome this limitation, a new procedure was developed, which allows physically motivated multiplication of the available reference data in order to obtain a sufficiently large dataset for training machine learning algorithms. The underlying example chemical synthesis was measured and analyzed with both application-relevant low-field NMR and high-field NMR spectroscopy as reference method. Artificial neural networks (ANNs) have the potential to infer valuable process information already from relatively limited input data. However, in order to predict the concentration at complex conditions (many reactants and wide concentration ranges), larger ANNs and, therefore, a larger Training dataset are required. We demonstrate that a moderately complex problem with four reactants can be addressed using ANNs in combination with the presented PAT method (low-field NMR) and with the proposed approach to generate meaningful training data. KW - Online NMR spectroscopy KW - Real-time process monitoring KW - Artificial neural networks KW - Automation KW - Process industry PY - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:b43-507508 SN - 1618-2642 VL - 412 IS - 18 SP - 4447 EP - 4459 PB - Springer CY - Berlin AN - OPUS4-50750 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 - Kern, Simon A1 - Liehr, Sascha A1 - Wander, Lukas A1 - Bornemann-Pfeiffer, Martin A1 - Müller, S. A1 - Maiwald, Michael A1 - Kowarik, Stefan T1 - Training data of quantitative online NMR spectroscopy for artificial neural networks N2 - Data set of low-field NMR spectra of continuous synthesis of nitro-4’-methyldiphenylamine (MNDPA). 1H spectra (43 MHz) were recorded as single scans. Two different approaches for the generation of artificial neural networks training data for the prediction of reactant concentrations were used: (i) Training data based on combinations of measured pure component spectra and (ii) Training data based on a spectral model. Synthetic low-field NMR spectra First 4 columns in MAT-files represent component areas of each reactant within the synthetic mixture spectrum. Xi (“pure component spectra dataset”) Xii (“spectral model dataset”) Experimental low-field NMR spectra from MNDPA-Synthesis This data set represents low-field NMR-spectra recorded during continuous synthesis of nitro-4’-methyldiphenylamine (MNDPA). Reference values from high-field NMR results are included. KW - NMR spectroscopy KW - Real-time process monitoring KW - Artificial neural networks KW - Online NMR spectroscopy KW - Automation KW - Process industry PY - 2020 U6 - https://doi.org/10.5281/zenodo.3677139 PB - Zenodo CY - Geneva AN - OPUS4-50456 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -