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NMR Diffusion and Relaxation for Monitoring of Degradation in Motor Oils

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Abstract

Different nuclear magnetic resonance (NMR) methods such as spectroscopy, diffusometry and relaxometry are applied with the aim to monitor motor oil degradation. Chemical degradation is detected by 1H NMR spectroscopy. With respect to quality control, low-field NMR is the established technique, which mostly uses relaxation and diffusion. Conventional methods such as mono-exponential data modeling lead to inadequate description of relaxation and diffusion data of complex fluids like motor oils. Inverse Laplace transform has difficulties in quantification, comparability and interpretation. Therefore, various data processing approaches are investigated to obtain the physico-chemically and numerically most correct description of the data. The gamma distribution model for diffusion and also for T 1 and T 2 relaxation data numerically describes the data with high accuracy. Three differently degraded motor oils were exemplarily investigated with regard to spectroscopic, relaxation and diffusion parameters.

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Acknowledgments

We are indebted to P. Galvosas (University of Wellington) and the Wellington group for access to the Inverse Laplace transform (ILT) implementation. The DFG is gratefully acknowledged for providing financial support for the joint instrumental facility center Pro2NMR of KIT and RWTH Aachen. Jan Küstner is thanked for performing the measurements at 20 MHz and Sarah Lüken for the measurements at 400 MHz.

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Authors and Affiliations

  1. Institute of Mechanical Process Engineering and Mechanics, Karlsruhe Institute of Technology, Adenauerring 20b, 76131, Karlsruhe, Germany

    Eva Förster & Hermann Nirschl

  2. Pro2NMR at IBG-4 and MVM, Karlsruhe Institute of Technology, Karlsruhe, Germany

    Hermann Nirschl & Gisela Guthausen

Authors
  1. Eva Förster
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  2. Hermann Nirschl
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  3. Gisela Guthausen
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Correspondence to Eva Förster.

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Förster, E., Nirschl, H. & Guthausen, G. NMR Diffusion and Relaxation for Monitoring of Degradation in Motor Oils. Appl Magn Reson 48, 51–65 (2017). https://doi.org/10.1007/s00723-016-0842-0

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  • DOI: https://doi.org/10.1007/s00723-016-0842-0

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