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Paramagnetic

Suppressed Reflectivity due to Spin-Controlled Localization in a Magnetic Semiconductor John F. DiTusa, Louisiana State University, DMR-0406140. Ferromagnetic. Fe 0.7 Co 0.3 Si. Fe 0.8 Co 0.2 Si. 0.96. Paramagnetic. Minority. 0.95. Optical Reflectivity. 0.94. T c. T c.

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Paramagnetic

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  1. Suppressed Reflectivity due to Spin-Controlled Localization in a Magnetic SemiconductorJohn F. DiTusa, Louisiana State University, DMR-0406140 Ferromagnetic Fe0.7Co0.3Si Fe0.8Co0.2Si 0.96 Paramagnetic Minority 0.95 Optical Reflectivity 0.94 Tc Tc Energy Landscape near bottom of FeSi conduction band 0.93 Majority 0 100 200 0 100 200 Temperature (K) Magnetic Semiconductors are attracting interest because of their potential use for spintronics. We have shown that a transition metal monosilicide, Fe1-xCoxSi, a half metallic magnet derived by doping the narrow gap semiconductor FeSi is less reflective in the magnetic state than above the transition temperature (TC). We conclude that the carriers in the majority spin sub-band scatter more strongly from the doping induced corrugated potential landscape than those in either the minority or paramagnetic bands due to the increased depth of the potential wells.

  2. Metal-Insulator Transition in Magnetic Semiconductors John F. DiTusa, Louisiana State University, DMR-0406140 Power Law Exponent of the Conductivity near the Insulator to Metal Transition Broader impact: Our ability to manipulate the magnetic moments of electrons to the same extent that we can their charge in modern device technologies underpins the nascent technology of spintronics. This research explores the fundamental question of how a paramagnetic semiconductor can be modified to exhibit ferromagnetic and metallic properties and searches for silicon-based materials for use in spintronics. This figure displays the unusual properties of the insulator to metal transition in Mn doped FeSi. 1.5 H = 0 1 T (K) 0.5 0 1.5 H = 9 T 1 T (K) Education: Undergraduate students Robert Anderson, Jakob Duran, Jon Hanson, Jon Hefner (computer science), Dung Lee, Becky Lefebvre, Andrew Morrow, Margaret Reaves (Chem E), Christopher Weaver, and Matthew Wolboldt and graduate students Song Guo and Rajan Rai all contribute to this research. 0.5 0 0 0.04 0.08 x

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