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Abstract
Imaging of structural defects in a material can be realized with a radio-frequency atomic magnetometer by monitoring the material’s response to a radio-frequency excitation field. We demonstrate two measurement configurations that enable the increase of the amplitude and phase contrast in images that represent a structural defect in electrically conductive and magnetically permeable samples. Both concepts involve the elimination of the excitation field component, orthogonal to the sample surface, from the atomic magnetometer signal. The first method relies on the implementation of a set of coils that directly compensates the excitation field component in the magnetometer signal. The second takes advantage of the fact that the radio-frequency magnetometer is not sensitive to the magnetic field oscillating along one of its axes. Results from simple modelling confirm the experimental observation and are discussed in detail.
Original language | English |
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Article number | 094503 |
Number of pages | 8 |
Journal | Journal of Applied Physics |
Volume | 125 |
Issue number | 9 |
Early online date | 1 Mar 2019 |
DOIs | |
Publication status | Published - 7 Mar 2019 |
Funding
P.B. was supported by the Engineering and Physical Sciences Research Council (EPSRC) (No. EP/P51066X/1).
Keywords
- imaging
- structural defects
- radio-frequency atomic magnetometer
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Dive into the research topics of 'Enhanced material defect imaging with a radio-frequency atomic magnetometer'. Together they form a unique fingerprint.Projects
- 1 Finished
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Industrial Case Account 2016 | Bevington, Patrick
Griffin, P. (Principal Investigator), Riis, E. (Co-investigator) & Bevington, P. (Research Co-investigator)
EPSRC (Engineering and Physical Sciences Research Council)
1/10/16 → 25/03/21
Project: Research Studentship Case - Internally allocated
Datasets
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Data for: "Enhanced material defect imaging with a radio-frequency atomic magnetometer"
Bevington, P. (Creator), Chalupczak, W. (Creator) & Gartman, R. (Creator), University of Strathclyde, 9 Oct 2019
DOI: 10.15129/d5f82b79-fc4e-4889-9082-4e33aa5b4514
Dataset