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 - 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 - 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 - JOUR A1 - Golub, Tino Petar A1 - Meyer, Klas A1 - Paul, Andrea A1 - Kipphardt, Heinrich A1 - Tuma, Dirk T1 - Exploring the potential of a setup for combined quantification of hydrogen in natural gas – Raman and NMR spectroscopy N2 - An accurate measurement of the amount fraction of hydrogen in gas mixtures is mandatory for practical applications, requiring methods that are fast, continuous, robust, and cost-effective. This study compares the performance of Raman and benchtop NMR process spectroscopy for determining the hydrogen amount fraction in gas mixtures. A setup was designed to integrate both techniques, enabling measurements of the same sample. Tests were conducted with gravimetrically prepared gas mixtures of reference quality ranging from 1.20 cmol/mol to 85.83 cmol/mol of hydrogen. The results demonstrate that Raman spectroscopy provides superior performance, with a minimal root mean square error (RMSE) of 0.22 cmol/mol and excellent linearity. In contrast, benchtop NMR spectroscopy faced challenges, such as overlapping peaks and longer measurement times, resulting in a higher RMSE of 0.71 cmol/mol. Raman spectroscopy proves to be particularly well-suited for practical applications due to its high accuracy and linearity. Meanwhile, benchtop NMR spectroscopy holds potential for future enhancements through ongoing technological advances, such as higher magnetic field strengths. In summary, the results from our study indicate that Raman spectroscopy is already a serviceable method for precise hydrogen quantification, whereas benchtop NMR spectroscopy can be attributed potential for future applications. KW - Hydrogen amount fraction KW - Raman spectroscopy KW - NMR spectroscopy KW - Field applicability PY - 2025 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-610093 DO - https://doi.org/10.1016/j.saa.2024.125087 SN - 1386-1425 VL - 325 SP - 1 EP - 9 PB - Elsevier B.V. CY - Amsterdam AN - OPUS4-61009 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Gottu Mukkula, A. R. A1 - Kern, Simon A1 - Salge, M. A1 - Holtkamp, M. A1 - Guhl, Svetlana A1 - Fleischer, C. A1 - Meyer, Klas A1 - Remelhe, M. A1 - Maiwald, Michael A1 - Engell, S. T1 - An Application of Modifier Adaptation with Quadratic Approximation on a Pilot Scale Plant in Industrial Environment N2 - The goal of this work is to identify the optimal operating input for a lithiation reaction that is performed in a highly innovative pilot scale continuous flow chemical plant in an industrial environment, taking into account the process and safety constraints. The main challenge is to identify the optimum operation in the absence of information about the reaction mechanism and the reaction kinetics. We employ an iterative real-time optimization scheme called modifier adaptation with quadratic approximation (MAWQA) to identify the plant optimum in the presence of plant-model mismatch and measurement noise. A novel NMR PAT-sensor is used to measure the concentration of the reactants and of the product at the reactor outlet. The experiment results demonstrate the capabilities of the iterative optimization using the MAWQA algorithm in driving a complex real plant to an economically optimal operating point in the presence of plant-model mismatch and of process and measurement uncertainties. KW - Process Analytical Technology KW - Online NMR Spectroscopy KW - Process Industry KW - Iterative real-time optimization KW - Modifier adaptation KW - Plant-model mismatch KW - Reactor control KW - CONSENS PY - 2020 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-524531 DO - https://doi.org/10.1016/j.ifacol.2020.12.685 SN - 1522-2640 VL - 53 IS - 2 SP - 11773 EP - 11779 PB - Elsevier CY - Amsterdam AN - OPUS4-52453 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Gugin, Nikita A1 - Yusenko, Kirill A1 - King, Andrew A1 - Meyer, Klas A1 - Al-Sabbagh, Dominik A1 - Villajos Collado, José Antonio A1 - Emmerling, Franziska T1 - Lighting up industrial mechanochemistry: Real-time in situ monitoring of reactive extrusion using energy-dispersive X-ray diffraction N2 - Mechanochemistry is an environmentally friendly synthetic approach that enables the sustainable production of a wide range of chemicals while reducing or eliminating the need for solvents. Reactive extrusion aims to move mechanochemistry from its conventional gram-scale batch reactions, typically performed in laboratory ball mills, to a continuous, large-scale process. Meeting this challenge requires in situ monitoring techniques to gain insights into reactive extrusion and its underlying processes. While the effectiveness of in situ Raman spectroscopy in providing molecular-level information has been demonstrated, our study uses energy-dispersive X-ray diffraction to monitor reactive extrusion in real time at the crystalline level. Our results provide previously unavailable control over the reactive extrusion process, promoting its perception as an industrially feasible green alternative to traditional solvent-based syntheses. KW - Mechanochemistry KW - Reactive extrusion KW - Green chemistry KW - In situ studies KW - Time-resolved in situ KW - TRIS KW - Synchrotron radiation KW - Scalable synthesis KW - Solid-state reactions KW - Reaction mechanisms PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-613969 DO - https://doi.org/10.1016/j.chempr.2024.07.033 VL - 10 IS - 11 SP - 1 EP - 16 PB - Elsevier B.V. AN - OPUS4-61396 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Kern, Simon A1 - Guhl, Svetlana A1 - Meyer, Klas A1 - Wander, Lukas A1 - Paul, Andrea A1 - Bremser, Wolfram A1 - Maiwald, Michael T1 - Mathematical and statistical tools for online NMR spectroscopy in chemical processes N2 - Monitoring chemical reactions is the key to chemical process control. Today, mainly optical online methods are applied, which require excessive calibration effort. NMR spectroscopy has a high potential for direct loop process control while exhibiting short set-up times. Compact NMR instruments make NMR spectroscopy accessible in industrial and harsh environ¬ments for advanced process monitoring and control, as demonstrated within the European Union’s Horizon 2020 project CONSENS. We present a range of approaches for the automated spectra analysis moving from conventional multivariate statistical approach, (i.e., Partial Least Squares Regression) to physically motivated spectral models (i.e., Indirect Hard Modelling and Quantum Mechanical calculations). By using the benefits of traditional qNMR experiments data analysis models can meet the demands of the PAT community (Process Analytical Technology) regarding low calibration effort/calibration free methods, fast adaptions for new reactants or derivatives and robust automation schemes. T2 - Advanced Mathematical and Computational Tools in Metrology and Testing conference CY - Glasgow, United Kingdom DA - 29.08.2017 KW - Online NMR Spectroscopy KW - Process Control KW - Partial Least Squares Regression KW - Indirect Hard Modelling KW - Quantum Mechanics KW - First Principles PY - 2018 SN - 978-9-813-27429-7 VL - 89 SP - 229 EP - 234 PB - World Scientific CY - New Jersey AN - OPUS4-51391 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -