MagViz: A Bottled Liquids Scanner Using Ultra-Low Field NMR Relaxometry

  • Robert Austin
  • Michelle Espy
  • Andrei Matlashov
  • Henrik Sandin
  • Larry Schultz
  • Algis Urbaitis
  • Petr Volegov
Conference paper
Part of the NATO Science for Peace and Security Series B: Physics and Biophysics book series (NAPSB)


Field Forensics, Inc. (FFI) has built a bottled liquids scanner utilizing ultra–low field NMR relaxometry. This device, called MagViz, is based upon a prototype developed at the Los Alamos National Laboratory (LANL) (Espy et al. Appl Supercond IEEE Trans 21(3):530, 2011; Espy et al. Supercond Sci Technol 23:034023. doi:10.1088/0953-2048/23/3/034023, 2010) [1, 2]. Despite using conventional Faraday detection coils in lieu of SQUIDs, MagViz, has demonstrated sufficient sensitivity to identify a number of threat liquids of interest to the Department of Homeland Security (Matlashov et al. Appl Supercond IEEE Trans 21(3):465–468, 2011) [3]. By accurate measurement of T1 and T2, liquids contained in opaque bottles and even non-ferromagnetic metal containers can be reliably identified. Protons are aligned using a 50 mT pre-polarizing field. T1 is determined in the pre-polarizing field, and T2 relaxation time is typically measured at 2,048 Hz in a 48 μT field. The coil assembly is contained within a table-top 0.79 m tall magnetically shielded enclosure. Although primarily intended for commercial and security applications, MagViz, works at Larmor frequencies that correspond to timescales that are characteristic of a host of interesting, slow, molecular dynamic processes like diffusion and intramolecular motion as well as biological processes such as protein folding, catalysis, and ligand binding and could conceivably serve as a COTS research instrument for fundamental studies in these areas.


Nuclear Magnetic Resonance Nuclear Magnetic Resonance Signal Local Magnetic Field Nuclear Magnetic Resonance Relaxation Liquid Explosive 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The work presented here accomplished under the support of Department of Homeland Security, Science and Technology Directorate under agreement HSHQPM12X00166. The authors would also like to personally thank Stephen Surko for his guidance at DHS, Valerie Lively and Paul Ruwaldt at TSL for helping with data collection, the support of LANL colleagues John Gomez, Shaun Newman, Mark Peters, Robert Sedillo, our FFI colleagues Al Guim, Mark Tesone, and special thanks to Lloyd Bastian for his expert electronics help.

LA-UR-12-24380Approved for public release; distribution is unlimited.


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Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  • Robert Austin
    • 1
  • Michelle Espy
    • 2
  • Andrei Matlashov
    • 2
  • Henrik Sandin
    • 2
  • Larry Schultz
    • 2
  • Algis Urbaitis
    • 2
  • Petr Volegov
    • 2
  1. 1.Field Forensics, Inc.Saint PetersburgUSA
  2. 2.Los Alamos National LaboratoryPhysics DivisionLos AlamosUSA

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