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Fe-Pd-X Thin Film-Polymer Composites for Sensor Applications - First-principles evaluation of magneto-crystalline anisotropy and related intrinsic properties of Fe-Pd-X: optimization of alloy composition

Fe-Pd-X Thin Film-Polymer Composites for Sensor Applications - First-principles evaluation of magneto-crystalline anisotropy and related intrinsic properties of Fe-Pd-X: optimization of alloy composition
用于传感器应用的 Fe-Pd-X 薄膜聚合物复合材料 - Fe-Pd-X 磁晶各向异性和相关固有特性的第一原理评估:合金成分的优化
批准号:
28259700
负责人:
Privatdozent Dr. Manuel Richter
金额:
$0.0万
依托单位国家:
德国
项目类别:
Priority Programmes
财政年份:
2006
资助国家:
德国
项目状态:
已结题
起止时间:
2005-12-31 至 2008-12-31
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英文摘要
This project is part of a collaboration aiming at new magnetic shape memory Fe-Pd-X alloys for sensor applications. Magnetic shape memory alloys (MSMA) have received much attention in recent years. They are of interest for actuator applications due to large magnetically induced strains of up to 10% in Ni2MnGa. However, application of this material is restricted due to low blocking forces. A promising MSMA is the Fe70Pd30 alloy with smaller strains but significantly higher blocking stresses of 10 MPa. Optimization of this alloy by adding third elements is necessary to facilitate its application in sensors. Here, first-principles calculations are envisaged to (i) gain fundamental understanding of the intrinsic properties (spontaneous magnetization, tetragonal distortion, magneto-crystalline anisotropy) of Fe-Pd alloys and (ii) predict the change of intrinsic properties if other elements are added to produce ternary (quaternary) Fe-Pd-X(-Y) systems. While the field-induced strain at low external field and vanishing external stress is proportional to the spontaneous magnetization, the achievable maximum strain and the blocking stress are unavoidably determined by the magneto-crystalline anisotropy energy. Thus, knowledge of both intrinsic properties is necessary to judge the technological applicability of a certain alloy. On the other hand, the magneto-crystalline anisotropy of MSMA is difficult to measure. Thus, first-principles calculations are needed both for the prediction of expected properties and for the understanding of measured data.
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