Finite-difference approach for the high-precision analysis of rotating-frame diffusion images.

doi:https://doi.org/10.1006/jmre.2000.2106

Title: Finite-difference approach for the high-precision analysis of rotating-frame diffusion images.

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Abstract

A finite-difference approach has been developed for precisely determining diffusion coefficient and T{sub 1} relaxation time in fluid samples analyzed by magnetization-grating rotating-frame imaging (MAGROFI) with either a surface coil or a toroid cavity detector (TCD). This approach avoids shortcomings of phenomenologically based approximations, such as neglect of sample geometries with singularities at the confines of the sample volume, and accounts for the diffusive edge enhancement observed in fluid imaging. Error limits are discussed. The new method has been applied to the determination of the self-diffusion coefficient for MAGROFI experiments using TCDs filled with acetone.

Authors:: Woelk, K; Zwank, B L. J.; Trautner, P; Lehnhof, E; Bargon, J; Klingler, R J; Gerald, II, R E; Rathke, J W

Publication Date:: 2000-01-01

Research Org.:: Argonne National Lab. (ANL), Argonne, IL (United States)

Sponsoring Org.:: USDOE Office of Science (SC); FOR

OSTI Identifier:: 942615

Report Number(s):: ANL/CMT/JA-33040
Journal ID: ISSN 1090-7807; JMARF3; TRN: US200920%%16

DOE Contract Number:: DE-AC02-06CH11357

Resource Type:: Journal Article

Journal Name:: J. Magn. Resonance

Additional Journal Information:: Journal Volume: 145; Journal Issue: 2000; Journal ID: ISSN 1090-7807

Country of Publication:: United States

Language:: ENGLISH

Subject:: 37 INORGANIC, ORGANIC, PHYSICAL AND ANALYTICAL CHEMISTRY; ACETONE; APPROXIMATIONS; IMAGES; RELAXATION TIME; SELF-DIFFUSION; FINITE DIFFERENCE METHOD; FLUIDS; ACCURACY

Citation Formats


                    Woelk, K, Zwank, B L. J., Trautner, P, Lehnhof, E, Bargon, J, Klingler, R J, Gerald, II, R E, Rathke, J W, Chemical Engineering, and Univ. of Bonn. Finite-difference approach for the high-precision analysis of rotating-frame diffusion images..  United States: N. p., 2000. 
        Web.  doi:10.1006/jmre.2000.2106.

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                    Woelk, K, Zwank, B L. J., Trautner, P, Lehnhof, E, Bargon, J, Klingler, R J, Gerald, II, R E, Rathke, J W, Chemical Engineering, & Univ. of Bonn. Finite-difference approach for the high-precision analysis of rotating-frame diffusion images..  United States.  https://doi.org/10.1006/jmre.2000.2106

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                    Woelk, K, Zwank, B L. J., Trautner, P, Lehnhof, E, Bargon, J, Klingler, R J, Gerald, II, R E, Rathke, J W, Chemical Engineering, and Univ. of Bonn. Sat .  
        "Finite-difference approach for the high-precision analysis of rotating-frame diffusion images.".  United States.  https://doi.org/10.1006/jmre.2000.2106.

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@article{osti_942615,

  title        = {Finite-difference approach for the high-precision analysis of rotating-frame diffusion images.},

  author       = {Woelk, K and Zwank, B L. J. and Trautner, P and Lehnhof, E and Bargon, J and Klingler, R J and Gerald, II, R E and Rathke, J W and Chemical Engineering and Univ. of Bonn},

  abstractNote = {A finite-difference approach has been developed for precisely determining diffusion coefficient and T{sub 1} relaxation time in fluid samples analyzed by magnetization-grating rotating-frame imaging (MAGROFI) with either a surface coil or a toroid cavity detector (TCD). This approach avoids shortcomings of phenomenologically based approximations, such as neglect of sample geometries with singularities at the confines of the sample volume, and accounts for the diffusive edge enhancement observed in fluid imaging. Error limits are discussed. The new method has been applied to the determination of the self-diffusion coefficient for MAGROFI experiments using TCDs filled with acetone.},

  doi          = {10.1006/jmre.2000.2106},

  url          = {https://www.osti.gov/biblio/942615},
  journal      = {J. Magn. Resonance},

  issn         = {1090-7807},

  number       = 2000,

  volume       = 145,

  place        = {United States},

  year         = {2000},

  month        = {1}

}

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