Chemische Charakterisierung und Spurenanalytik
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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.
The catalytic behavior of iron phthalocyanine (FePc)-sensitized magnetic nanocatalysts was evaluated for their application in the oxidative treatment of Bisphenol A (BPA) under mild environmental conditions. Two types of FePc (Fe(II)Pc and Fe(III)Pc), which are highly photosensitive compounds, were immobilized on the surface of functionalized magnetite. The nanomaterials were characterized by high resolution transmission electron microscopy (HR-TEM), X-ray difraction (XRD), Fourier transform infrared spectroscopy (FTIR) and thermogravimetric analyses (TGA). The generation of singlet Oxygen by nanomaterials was also investigated. In the presence of UVA light exposure (365nm) and 15mM H2O2, the M@Fe(III)Pc photocatalyst gave the best results; for a catalyst concentration of 2.0gL −1, around 60% BPA was removed after 120min of reaction. These experimental conditions were further tested under natural solar light exposure, for which also M@Fe(III)Pc exhibited enhanced oxidative catalytic activity, being able to remove 83% of BPA in solution. The water samples were less cytotoxic after treatment, this being confrmed by the MCF-7 cell viability assay.
In case study one of the CONSENS project, two aromatic substances were coupled by a lithiation reaction, which is a prominent example in pharmaceutical industry. The two aromatic reactants (Aniline and o-FNB) were mixed with Lithium-base (LiHMDS) in a continuous modular plant to produce the desired product (Li-NDPA) and a salt (LiF). The salt precipitates which leads to the formation of particles. The feed streams were subject to variation to drive the plant to its optimum.
The uploaded data comprises the results from four days during continuous plant operation time. Each day is denoted from day 1-4 and represents the dates 2017-09-26, 2017-09-28, 2017-10-10, 2017-10-17.
In the following the contents of the files are explained.
Flexible automation with compact NMR spectroscopy for continuous production of pharmaceuticals
(2019)
Modular plants using intensified continuous processes represent an appealing concept for the production of pharmaceuticals. It can improve quality, safety, sustainability, and profitability compared to batch processes; besides, it enables plug-and-produce reconfiguration for fast product changes. To facilitate this flexibility by real-time quality control, we developed a solution that can be adapted quickly to new processes and is based on a compact nuclear magnetic resonance (NMR) spectrometer. The NMR sensor is a benchtop device enhanced to the requirements of automated chemical production including robust evaluation of sensor data. Beyond monitoring the product quality, online NMR data was used in a new iterative optimization approach to maximize the plant profit and served as a reliable reference for the calibration of a near-infrared (NIR) spectrometer. The overall approach
was demonstrated on a commercial-scale pilot plant using a metal-organic reaction with pharmaceutical relevance.
Die Untersuchungen sind durch das das DFG Schwerpunktprogramm 1374 für Biodiversitätsforschung motiviert. Anhand des überwachten Abbaus von Totholz soll untersucht werden, welche Auswirkung unterschiedliche Formen und Intensitäten der Landnutzung auf die Biodiversität innerhalb von drei Exploratorien zeigen. Bei den vorangegangenen Studien wurden Nahrungs¬netzte auf organismischer Ebene sowie Klima- und Umgebungsdaten untersucht und die Veränderung des ausgebrachten Totholzes als Masseverlust erfasst. Es wird jedoch erwartet, dass in Anhängigkeit der beteiligten Organismen und Umgebungsparameter verschiedene Mechanismen des Holzabbaus vorliegen, die in unterschiedlichen Abbauraten der Holzkomponenten Lignin, Cellulose und Hemicellulose resultieren. Aus diesem Grund ist die quantitative Untersuchung des Ligningehalts an Hölzern ein wesentlicher Schritt für ein verbessertes Verständnis der Biodiversität in den untersuchten Exploratorien. Um der hohen Probenanzahl, die sich aus der Kontrolle von jeweils 100 Proben (Plots) in den drei Exploratorien für Hölzer aus 13 Baumarten ergibt, gerecht zu werden, wird eine schnelle und effiziente Untersuchungsmethode benötigt. Diese wird derzeit in Form einer NIR-spektroskopischen Bestimmung des Ligningehalts entwickelt. Um basierend auf den NIR Spektren vermahlener Holzproben Lignin und weitere Holzkomponenten korrekt vorherzusagen, muss zunächst basierend auf nasschemischen Analysen ein chemometrisches Modell kalibriert werden. Dazu ist die Charakterisierung von mindestens 20 – 30 Proben, die eine hohe Varianz des Ligningehalts aufweisen, notwendig. Im Vortrag werden erste Untersuchungs¬ergebnisse für die Vorhersage des Ligningehalts von Kiefernhölzern, die für ein Jahr in dem Exploratorium Schorfheide exponiert wurden, vorgestellt. Die anschließende explorative Datenanalyse (PCA) unter Verwendung von insgesamt 28 Variablen zur Beschreibung klimatischer und Umgebungsparameter weist eine Reihe interessanter Korrelationen auf. Demnach ist der Ligningehalt auf der ersten Hauptkomponente (29% erklärte Varianz) positiv mit dem C/N-Verhältnis und negativ mit Landnutzungsindex korreliert. Masseverlust und Temperatur hingegen dominieren die zweite Hauptkomponente (12% erklärte Varianz).
Monitoring specific chemical properties is the key to chemical process control. Today, mainly optical online methods are applied, which require time- and cost-intensive calibration effort. NMR spectroscopy, with its advantage being a direct comparison method without need for calibration, has a high potential for closed-loop process control while exhibiting short set-up times. Compact NMR instruments make NMR spectroscopy accessible in industrial and rough environments for process monitoring and advanced process control strategies.
We present a fully automated data analysis approach which is completely based on physically motivated spectral models as first principles information (Indirect Hard Modelling – IHM) and applied it to a given pharmaceutical lithiation reaction in the framework of the European Union’s Horizon 2020 project CONSENS. Online low-field NMR (LF NMR) data was analysed by IHM with low calibration effort, compared to a multivariate PLS-R (Partial Least Squares Regression) approach, and both validated using online high-field NMR (HF NMR) spectroscopy.