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Spin Exchange Relaxation Free (SERF) Magnetometers

  • Igor Mykhaylovich SavukovEmail author
Chapter
Part of the Smart Sensors, Measurement and Instrumentation book series (SSMI, volume 19)

Abstract

A little more than a decade ago spin-exchange relaxation free (SERF) magnetometers set a new record of magnetic field sensitivity surpassing cryogenic SQUIDs. Since then a lot of progress has been made in design, commercialization, and development of novel applications of the SERF magnetometers. In addition, the operation of the SERF magnetometer was extended beyond the SERF regime resulting in the discovery of ultra-high sensitivity high frequency and scalar magnetometers. This chapter will cover some basic principles of SERF and high-density SERF-like magnetometers in the regimes when spin-exchange collisions affect the line-width of the magnetometers. Various topics will be covered: the SERF operation, the role of spin-exchange collisions, fundamental and technical noises in SERF and other high-density magnetometers, light shifts, optical pumping. The formalism of density matrix equations will be briefly described with some illustrations. At some conditions, Bloch equations can also provide adequate treatment of spin dynamics, so this topic is also briefly covered. Some applications, such as magnetoencephalography and magnetic resonance imaging (MRI), of SERF, high-frequency, and scalar magnetometers will be discussed. The number of applications will grow in the future, especially when high-sensitivity SERF magnetometers become commercially available and their operation becomes simple and user-friendly. Finally, it is anticipated that in the near future many applications developed with SQUIDs will be gradually replaced with those based on SERF and other ultra-sensitive atomic magnetometers.

Keywords

Probe Beam Polarize Beam Splitter Bloch Equation Light Shift Technical Noise 
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.

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Copyright information

© Springer International Publishing Switzerland 2017

Authors and Affiliations

  1. 1.Los Alamos National LaboratoryLos AlamosUSA

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