TY - GEN A1 - Bernstein, Michael A1 - Diehl, Bernd W. K. A1 - Holzgrabe, Ulrike A1 - Maiwald, Michael A1 - Meyer, Klas A1 - Monakhova, Yulia A1 - Schönberger, Torsten T1 - qNMR - The handbook N2 - Quantitative NMR (qNMR) has been around for a long time, but also has great potential to solve future problems in any quantitative analysis. As a primary method, it differs fundamentally from chromatographic methods: it is better described as a quantum mechanical balance. Succesful implementation of qNMR requires certain attention to detail. "qNMR - the handbook" is intended to be a guide for analysts to help understand the fundamental principles of NMR and the significant points relating to its implementation for quantitation. Regulatory considerations of qNMR adoption are explained. NMR fundamentals are explained to provide understanding. Together with many useful examples, the book is a compelling addition to the laboratory's reference library, providing all the tools that any practitioner should know to successfully implement qNMR. The authors are qNMR pioneers and come from a variety of backgrounds including business, government and academia. KW - Quantitative NMR spectroscopy KW - NMR spectroscopy KW - Handbook KW - qNMR PY - 2023 SN - 978-3-7568-7891-8 SN - 978-3-7583-8050-1 SP - 1 EP - 302 PB - BoD – Books on Demand CY - Norderstedt AN - OPUS4-59839 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Bornemann-Pfeiffer, Martin A1 - Kern, Simon A1 - Maiwald, Michael A1 - Meyer, Klas T1 - Calibration-Free Chemical Process and Quality Control Units as Enablers for Modular Production N2 - Modular chemical production is a tangible translation of the digital transformation of the process industry for specialty chemicals. In particular, it enables the speeding-up of process development and thus a quicker time to market by flexibly connecting and orchestrating standardised physical modules and bringing them to life (i.e., parameterising them) with digitally accumulated process knowledge. We focus on the specific challenges of chemical process and quality control, which in its current form is not well suited for modular production and provide possible approaches and examples of the change towards direct analytical methods, analytical model transfer or machine-supported processes. KW - Modular Production KW - Chemical Process Control KW - Process Analytical Technology KW - Digital Transformation KW - Industry 4.0 PY - 2021 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-517264 DO - https://doi.org/10.1002/cite.202000150 SN - 1522-2640 VL - 93 IS - 1-2 SP - 62 EP - 70 PB - Wiley-VCH CY - Weinheim AN - OPUS4-51726 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Bornemann-Pfeiffer, Martin A1 - Meyer, Klas A1 - Lademann, J. A1 - Kraume, M. A1 - Maiwald, Michael T1 - Contributions towards variable temperature shielding for compact NMR instruments N2 - The application of compact NMR instruments to hot flowing samples or exothermically reacting mixtures is limited by the temperature sensitivity of permanent magnets. Typically, such temperature effects directly influence the achievable magnetic field homogeneity and hence measurement quality. The internal-temperature control loop of the magnet and instruments is not designed for such temperature compensation. Passive insulation is restricted by the small dimensions within the magnet borehole. Here, we present a design approach for active heat shielding with the aim of variable temperature control of NMR samples for benchtop NMR instruments using a compressed airstream which is variable in flow and temperature. Based on the system identification and surface temperature measurements through thermography, a model predictive control was set up to minimise any disturbance effect on the permanent magnet from the probe or sample temperature. This methodology will facilitate the application of variable-temperature shielding and, therefore, extend the application of compact NMR instruments to flowing sample temperatures that differ from the magnet temperature. KW - Benchtop NMR KW - Continuous Processes KW - Inline Analytics KW - Model Predictive Control KW - Process Analytical Tecnology KW - Temperature Control PY - 2023 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-579219 DO - https://doi.org/10.1002/mrc.5379 SN - 1097-458X SP - 1 EP - 10 PB - John Wiley & Sons Ltd. AN - OPUS4-57921 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Bornemann-Pfeiffer, Martin A1 - Wolf, Jakob A1 - Meyer, Klas A1 - Kern, S. A1 - Angelone, D. A1 - Leonov, A. A1 - Cronin, L. A1 - Emmerling, Franziska T1 - Standardization and control of Grignard reactions in a universal chemical synthesis machine using online NMR T1 - Standardisierung und Kontrolle von Grignard-Reaktionen mittels Online-NMR in einer universellen chemischen Syntheseplattform N2 - A big problem with the chemistry literature is that it is not standardized with respect to precise operational parameters, and real time corrections are hard to make without expert knowledge. This lack of context means difficult reproducibility because many steps are ambiguous, and hence depend on tacit knowledge. Here we present the integration of online NMR into an automated chemical synthesis machine (CSM aka. “Chemputer” which is capable of small-molecule synthesis using a universal programming language) to allow automated analysis and adjustment of reactions on the fly. The system was validated and benchmarked by using Grignard reactions which were chosen due to their importance in synthesis. The system was monitored in real time using online-NMR, and spectra were measured continuously during the reactions. This shows that the synthesis being done in the Chemputer can be dynamically controlled in response to feedback optimizing the reaction conditions according to the user requirements. N2 - Ein Problem der chemischen Literatur ist die fehlende Standardisierung bezüglich genauer Bedingungen, auch Echtzeit-Korrekturen sind ohne Expertenwissen nur schwer möglich. Dieser Mangel an Details erschwert experimentelle Reproduzierbarkeit, da Schritte oft mehrdeutig sind und daher von implizitem Wissen abhängen. Hier präsentieren wir die Integration von Online-NMR Spektroskopie in eine automatisierte chemische Syntheseplattform (CSM aka. “Chemputer”, unter Verwendung einer universellen Programmiersprache zur Synthese kleiner Moleküle fähig), um eine automatisierte Analyse und Anpassung von Reaktionen im laufenden Betrieb zu ermöglichen. Das System wurde anhand von Grignard-Reaktionen, die aufgrund ihrer Bedeutung für die Synthese ausgewählt wurden, validiert und einem Härtetest unterzogen. Synthesen wurden in Echtzeit mit Online-NMR überwacht, und die Spektren wurden während der Reaktionen kontinuierlich aufgenommen und analysiert. Dies zeigt, dass der Chemputer dynamisch mittels einer Regelung kontrolliert werden kann, um die Reaktionsbedingungen entsprechend den Anforderungen des Benutzers zu optimieren. KW - Grignard reaction KW - NMR spectroscopy KW - Process analytical technology KW - Process control KW - Grignard-Reaktion KW - NMR-Spektroskopie KW - Prozessanalytik KW - Prozesskontrolle PY - 2021 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-531260 DO - https://doi.org/10.1002/anie.202106323 SN - 1521-3773 SN - 1433-7851 N1 - Bibliografische Angaben für die deutsche Version: Angewandte Chemie 2021, Jg. 133, S. 1–7, ISSN 0044-8249, ISSN 1521-3757, https://doi.org/10.1002/ange.202106323 - Bibliographic information for the German version: Angewandte Chemie 2021, vol. 133, p. 1–7, ISSN 0044-8249, ISSN 1521-3757, https://doi.org/10.1002/ange.202106323 VL - 60 IS - 43 SP - 1 EP - 6 PB - Wiley-VCH CY - Weinheim AN - OPUS4-53126 LA - mul AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Bruno, Francesco A1 - Fiorucci, Letizia A1 - Vignoli, Alessia A1 - Meyer, Klas A1 - Maiwald, Michael A1 - Ravera, Enrico T1 - pyIHM: Indirect Hard Modeling, in Python N2 - NMR is a powerful analytical technique that combines an exquisite qualitative power, related to the unicity of the spectra of each molecule in a mixture, with an intrinsic quantitativeness, related to the fact that the integral of each peak only depends on the number of nuclei (i.e., the amount of substance times the number of equivalent nuclei in the signal), regardless of the molecule. Signal integration is the most common approach in quantitative NMR but has several drawbacks (vide infra). An alternative is to use hard modeling of the peaks. In this paper, we present pyIHM, a Python package for the quantification of the components of NMR spectra through indirect hard modeling, and we discuss some numerical details of the implementation that make this approach robust and reliable. KW - Algorithms KW - Chemical Structure KW - Deconvolution KW - Mixtures KW - NMR spectroscopy PY - 2025 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-626793 DO - https://doi.org/10.1021/acs.analchem.4c06484 SN - 1520-6882 VL - 97 IS - 8 SP - 4598 EP - 4605 PB - ACS Publications CY - Washington D.C. AN - OPUS4-62679 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Chandna, Sanjam A1 - Povolotsky, Tatyana L. A1 - Nie, Chuanxiong A1 - Schwartz, Sophia A1 - Wedepohl, Stefanie A1 - Quaas, Elisa A1 - Ludwig, Kai A1 - Boyakova, Yulia A1 - Bhatia, Sumati A1 - Meyer, Klas A1 - Falkenhagen, Jana A1 - Haag, Rainer A1 - Block, Stephan T1 - Lignin-Based Mucus-Mimicking Antiviral Hydrogels with Enzyme Stability and Tunable Porosity N2 - Mucus is a complex hydrogel that acts as a defensive and protective barrier in various parts of the human body. Therise in the level of viral infections has underscored the importance of advancing research into mucus-mimicking hydrogels for theefficient design of antiviral agents. Herein, we demonstrate the gram-scale synthesis of biocompatible, lignin-based virus-bindinginhibitors that reduce waste and ensure long-term availability. The lignin-based inhibitors are equipped with sulfate moieties, whichare known binding partners for many viruses, including SARS-CoV-2 and herpes viruses. In addition, cross-linking the synthesizedinhibitors yielded hydrogels that mimicked native mucus concerning surface functionality and rheology. The degree of sulfationexhibits a very strong impact on the mesh size distribution of the hydrogels, which provides a new means to fine-tune the steric andelectrostatic contributions of the virus−hydrogel interaction. This feature strongly impacts the sequestration capability of the lignin-based hydrogels, which is demonstrated by infection inhibition assays involving human herpes simplex virus 1, influenza A viruses,and the bacterium Escherichia coli (E. coli). These measurements showed a reduction in plaque-forming units (HSV-1) and colony-forming units (E. coli) by more than 4 orders of magnitude, indicating the potent inhibition by the lignin-based hydrogels KW - Lignin functionalization KW - Mucus-mimicking hydrogels PY - 2025 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-625301 DO - https://doi.org/10.1021/acsami.4c18519 SP - 1 EP - 14 PB - American Chemical Society (ACS) CY - USA AN - OPUS4-62530 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Esche, E. A1 - Kraemer, B. A1 - Müller, D. A1 - Meyer, Klas A1 - Zientek, Nicolai A1 - Maiwald, Michael A1 - Wozny, G. T1 - Improved desorption control via Raman spectroscopy N2 - In this contribution a Raman spectrometer based control structure for the heating of a desorption column is proposed. For this purpose calibration experiments for the absorption of carbon dioxide using monoethanolamine solutions are carried out and calibration models are developed to measure both carbon dioxide liquid loads and monoethanolamine mass fractions. The calibration experiments are supported by online NMR spectroscopy to accurately measure the appearance of all species in the electrolyte system. Both models are tested during the plant operation of a mini-plant for the oxidative coupling of methane and the proof of concept for the control structure is given. The Raman spectroscopy implemented in the ATEX conform mini-plant shows a reliable and robust performance being even indifferent to impurities hindering the GC analysis. T2 - 20th International Conference of Process Engineering and Chemical Plant Design CY - Berlin, Germany DA - 15.10.2014 KW - Absorption KW - Carbon capture KW - Raman spectroscopy KW - desorption control KW - Prozessanalytik KW - Process analytical technology PY - 2014 UR - https://www.verfahrenstechnik.tu-berlin.de/fileadmin/fg158/Dokumente/Manuskripte/2014/20th-ICPEPD-Proceedings.pdf SN - 978-3-00-047364-7 SP - 223 EP - 233 PB - Technische Universität Berlin CY - Berlin AN - OPUS4-37399 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 - 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 - 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 -