E
E. Dan Dahlberg
Researcher at University of Minnesota
Publications - 127
Citations - 2262
E. Dan Dahlberg is an academic researcher from University of Minnesota. The author has contributed to research in topics: Magnetization & Magnetic anisotropy. The author has an hindex of 24, co-authored 123 publications receiving 2171 citations.
Papers
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Journal ArticleDOI
Use of the anisotropic magnetoresistance to measure exchange anisotropy in Co/CoO bilayers
B. H. Miller,E. Dan Dahlberg +1 more
TL;DR: In this article, anisotropic magnetoresistance was used to measure the angle the magnetization makes relative to the easy direction, which gave energies larger by a factor of 2 than the traditional measurements of the shift in the hysteresis loop (an irreversible process) of the ferromagnetic layer in bilayer samples of Co/CoO.
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Magnetization reversal in (100) Fe thin films.
J. M. Florczak,E. Dan Dahlberg +1 more
TL;DR: In this article, the authors used the longitudinal and transverse magneto-optical Kerr effects to detect two orthgonal in-plane magnetization components in single crystal Fe/GaAs (100) thin films.
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Detecting two magnetization components by the magneto‐optical Kerr effect
J. M. Florczak,E. Dan Dahlberg +1 more
TL;DR: In this paper, a novel technique was presented for detecting two orthogonal in-plane magnetization components, which are parallel and perpendicular to the plane of incidence of the light beam.
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Magnetotransport: An ideal probe of anisotropy energies in epitaxial films (invited)
TL;DR: Magnetotransport measurements provide an ideal probe to determine the various anisotropy energies in epitaxial magnetic films as discussed by the authors, and both the EHE and the AMR have been used to study the magnetic properties.
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Micromagnetic Microscopy and Modeling
E. Dan Dahlberg,Jian-Gang Zhu +1 more
TL;DR: In this article, an image of rows of bits in a magneto-optical medium were both written and imaged using a magnetic force microscope and the results on this length scale provide information that can be used in models and also challenge models' predictive capabilities.