Literature DB >> 29616294

Online low-field NMR spectroscopy for process control of an industrial lithiation reaction-automated data analysis.

Simon Kern1,2, Klas Meyer1, Svetlana Guhl1, Patrick Gräßer1, Andrea Paul1, Rudibert King2, Michael Maiwald3.   

Abstract

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 enabling 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 modeling-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 analyzed 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. Graphical abstract NMR sensor module for monitoring of the aromatic coupling of 1-fluoro-2-nitrobenzene (FNB) with aniline to 2-nitrodiphenylamine (NDPA) using lithium-bis(trimethylsilyl) amide (Li-HMDS) in continuous operation. Online 43.5 MHz low-field NMR (LF) was compared to 500 MHz high-field NMR spectroscopy (HF) as reference method.

Entities:  

Keywords:  Benchtop NMR spectroscopy; Indirect hard modeling; Online NMR spectroscopy; Partial least squares regression; Process analytical technology; Smart sensors

Year:  2018        PMID: 29616294     DOI: 10.1007/s00216-018-1020-z

Source DB:  PubMed          Journal:  Anal Bioanal Chem        ISSN: 1618-2642            Impact factor:   4.142


  3 in total

1.  Artificial neural networks for quantitative online NMR spectroscopy.

Authors:  Simon Kern; Sascha Liehr; Lukas Wander; Martin Bornemann-Pfeiffer; Simon Müller; Michael Maiwald; Stefan Kowarik
Journal:  Anal Bioanal Chem       Date:  2020-05-09       Impact factor: 4.142

2.  Benchtop NMR for Online Reaction Monitoring of the Biocatalytic Synthesis of Aromatic Amino Alcohols.

Authors:  C Claaßen; K Mack; D Rother
Journal:  ChemCatChem       Date:  2020-01-20       Impact factor: 5.686

3.  Autonomous Multi-Step and Multi-Objective Optimization Facilitated by Real-Time Process Analytics.

Authors:  Peter Sagmeister; Florian F Ort; Clemens E Jusner; Dominique Hebrault; Thomas Tampone; Frederic G Buono; Jason D Williams; C Oliver Kappe
Journal:  Adv Sci (Weinh)       Date:  2022-02-01       Impact factor: 16.806

  3 in total

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