Advertisement

Analytical and Bioanalytical Chemistry

, Volume 410, Issue 14, pp 3349–3360 | Cite as

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

  • Simon Kern
  • Klas Meyer
  • Svetlana Guhl
  • Patrick Gräßer
  • Andrea Paul
  • Rudibert King
  • Michael Maiwald
Research Paper

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

Keywords

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

Notes

Acknowledgements

The authors thank Lukas Wander for his help plotting Fig. 3.

Funding information

This study was supported by the funding of CONSENS by the European Union’s Horizon 2020 research and innovation program under grant agreement No. 636942.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Supplementary material

216_2018_1020_MOESM1_ESM.pdf (977 kb)
ESM 1 (PDF 976 kb)

References

  1. 1.
    Wiles C, Watts P. Continuous flow reactors: a perspective. Green Chem. 2012;14(1):38.CrossRefGoogle Scholar
  2. 2.
    Adamo A, Beingessner RL, Behnam M, Chen J, Jamison TF, Jensen KF, et al. On-demand continuous-flow production of pharmaceuticals in a compact, reconfigurable system. Science. 2016;352(6281):61.CrossRefPubMedGoogle Scholar
  3. 3.
    Zhang P, Terefenko EA, McComas CC, Mahaney PE, Vu A, Trybulski E, et al. Synthesis and activity of novel 1- or 3-(3-amino-1-phenyl propyl)-1,3-dihydro-2H-benzimidazol-2-ones as selective norepinephrine reuptake inhibitors. Bioorg Med Chem Lett. 2008;18(23):6067.CrossRefPubMedGoogle Scholar
  4. 4.
    Bieringer T, Buchholz S, Kockmann N. Future production concepts in the chemical industry: modular–small-scale–continuous. Chem Eng Technol. 2013;36(6):900.CrossRefGoogle Scholar
  5. 5.
    Chinnusamy T, Yudha SS, Hager M, Kreitmeier P, Reiser O. Application of metal-based reagents and catalysts in microstructured flow devices. ChemSusChem. 2012;5(2):247.CrossRefPubMedGoogle Scholar
  6. 6.
    Meyer K, Kern S, Zientek N, Guthausen G, Maiwald M. Process control with compact NMR. TrAC Trends Anal Chem. 2016;83(Part A):39.CrossRefGoogle Scholar
  7. 7.
    Maiwald M, Gräßer P, Wander L, Zientek N, Guhl S, Meyer K, et al. Strangers in the night—smart process sensors in our current automation landscape. PRO. 2017;1:628.  https://doi.org/10.3390/proceedings1040628.CrossRefGoogle Scholar
  8. 8.
    Edwards JC, Giammatteo PJ. In: Bakeev KA, editor. Process Analytical Technology. Hoboken: John Wiley & Sons, Ltd; 2010.  https://doi.org/10.1002/9780470689592.ch10.CrossRefGoogle Scholar
  9. 9.
    Ferstl W, Klahn T, Schweikert W, Billeb G, Schwarzer M, Loebbecke S. Inline analysis in microreaction technology: a suitable tool for process screening and optimization. Chem Eng Technol. 2007;30(3):370.CrossRefGoogle Scholar
  10. 10.
    Leung S-A, Winkle RF, Wootton RCR, de Mello AJ. A method for rapid reaction optimisation in continuous-flow microfluidic reactors using online Raman spectroscopic detection. Analyst. 2005;130(1):46.CrossRefPubMedGoogle Scholar
  11. 11.
    Floyd TM, Schmidt MA, Jensen KF. Silicon micromixers with infrared detection for studies of liquid-phase reactions. Ind Eng Chem Res. 2005;44(8):2351.CrossRefGoogle Scholar
  12. 12.
    Markley JL. NMR analysis goes nano. Nat Biotechnol. 2007;25(7):750.CrossRefPubMedGoogle Scholar
  13. 13.
    Maiwald M, Fischer HH, Kim Y-K, Albert K, Hasse H. Quantitative high-resolution on-line NMR spectroscopy in reaction and process monitoring. J Magn Reson. 2004;166(2):135.CrossRefPubMedGoogle Scholar
  14. 14.
    Zientek N, Laurain C, Meyer K, Kraume M, Guthausen G, Maiwald M. Simultaneous 19F–1H medium resolution NMR spectroscopy for online reaction monitoring. J Magn Reson. 2014;249:53.CrossRefPubMedGoogle Scholar
  15. 15.
    Mitchell J, Gladden LF, Chandrasekera TC, Fordham EJ. Low-field permanent magnets for industrial process and quality control. Prog Nucl Magn Reson Spectrosc. 2014;76:1.CrossRefPubMedGoogle Scholar
  16. 16.
    Zalesskiy SS, Danieli E, Blümich B, Ananikov VP. Miniaturization of NMR systems: desktop spectrometers, microcoil spectroscopy, and “NMR on a Chip” for chemistry, biochemistry, and industry. Chem Rev. 2014;114(11):5641.CrossRefPubMedGoogle Scholar
  17. 17.
    Dalitz F, Cudaj M, Maiwald M, Guthausen G. Process and reaction monitoring by low-field NMR spectroscopy. Prog Nucl Magn Reson Spectrosc. 2012;60:52.CrossRefPubMedGoogle Scholar
  18. 18.
    Blümich B. Introduction to compact NMR: a review of methods. TrAC Trends Anal Chem. 2016;83(Part A):2.CrossRefGoogle Scholar
  19. 19.
    Singh K, Blümich B. NMR spectroscopy with compact instruments. TrAC Trends Anal Chem. 2016;83(Part A):12.CrossRefGoogle Scholar
  20. 20.
    Gouilleux B, Charrier B, Danieli E, Dumez J-N, Akoka S, Felpin F-X, et al. Real-time reaction monitoring by ultrafast 2D NMR on a benchtop spectrometer. Analyst. 2015;140(23):7854.CrossRefPubMedGoogle Scholar
  21. 21.
    Wynn DA, Roth MM, Pollard BD. The solubility of alkali-metal fluorides in non-aqueous solvents with and without crown ethers, as determined by flame emission spectrometry. Talanta. 1984;31(11):1036.CrossRefPubMedGoogle Scholar
  22. 22.
    Mazet V, Carteret C, Brie D, Idier J, Humbert B. Background removal from spectra by designing and minimising a non-quadratic cost function. Chemom Intell Lab Syst. 2005;76(2):121.CrossRefGoogle Scholar
  23. 23.
    Chen L, Weng ZQ, Goh LY, Garland M. An efficient algorithm for automatic phase correction of NMR spectra based on entropy minimization. J Magn Reson. 2002;158(1–2):164.CrossRefGoogle Scholar
  24. 24.
    Savorani F, Tomasi G, Engelsen SB. icoshift: a versatile tool for the rapid alignment of 1D NMR spectra. J Magn Reson. 2010;202(2):190.CrossRefPubMedGoogle Scholar
  25. 25.
    Michalik-Onichimowska A, Kern S, Riedel J, Panne U, King R, Maiwald M. “Click” analytics for “click” chemistry—a simple method for calibration–free evaluation of online NMR spectra. J Magn Reson. 2017;277:154.CrossRefPubMedGoogle Scholar
  26. 26.
    Kriesten E, Alsmeyer F, BardoW A, Marquardt W. Fully automated indirect hard modeling of mixture spectra. Chemom Intell Lab Syst. 2008;91(2):181.CrossRefGoogle Scholar
  27. 27.
    Dondoni A, Giovannini PP, Massi A. Assembling heterocycle-tethered C-glycosyl and alpha-amino acid residues via 1,3-dipolar cycloaddition reactions. Org Lett. 2004;6(17):2929.CrossRefPubMedGoogle Scholar
  28. 28.
    Kessler W. Multivariate Datenanalyse. Weinheim: Wiley-VCH Verlag GmbH & Co KGaA; 2006.  https://doi.org/10.1002/9783527610037.ch4.CrossRefGoogle Scholar
  29. 29.
    Westad F, Marini F. Validation of chemometric models—a tutorial. Anal Chim Acta. 2015;893:14.CrossRefPubMedGoogle Scholar
  30. 30.
    Zientek N, Laurain C, Meyer K, Paul A, Engel D, Guthausen G, et al. Automated data evaluation and modelling of simultaneous 19F–1H medium-resolution NMR spectra for online reaction monitoring. Magn Reson Chem. 2015;  https://doi.org/10.1002/mrc.4216.

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Simon Kern
    • 1
    • 2
  • Klas Meyer
    • 1
  • Svetlana Guhl
    • 1
  • Patrick Gräßer
    • 1
  • Andrea Paul
    • 1
  • Rudibert King
    • 2
  • Michael Maiwald
    • 1
  1. 1.Division Process Analytical TechnologyBundesanstalt für Materialforschung und -prüfung (BAM)BerlinGermany
  2. 2.Department Measurement and Control, Institute of Process EngineeringBerlin University of TechnologyBerlinGermany

Personalised recommendations