Experimental Validation of Ferromagnetic–Antiferromagnetic Competition in Fe x Zn 1–x Se Quantum Dots by Computational Modeling
Experimental Validation of Ferromagnetic–Antiferromagnetic Competition in Fe x Zn 1–x Se Quantum Dots by Computational Modeling
复制标题
通过计算模型对 Fe x Zn 1-x Se 量子点中铁磁-反铁磁竞争的实验验证
DOI：
10.1021/acs.chemmater.8b00143
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发表时间：
2018
影响因子：
8.6
通讯作者：
Strouse, Geoffrey F.
中科院分区：
文献类型：
--
作者：
Bindra, Jasleen K.;Gutsev, Lavrenty Gennady;Van Tol, Johan;Singh, Kedar;Dalal, Naresh S.;Strouse, Geoffrey F.
传统的计算方法已经被用来解释观察到的新的材料性能。使用计算模型来预测量子点（QD）在其合成制备之前的这种性质的发生将促进新材料的快速开发。我们证明，使用计算建模可以允许基于铁掺杂的ZnSe的磁性半导体量子点的设计之前的样品的制备。DFT模型预测形成的多核Fe簇内的10%Fe掺杂的ZnSe量子点，以减轻晶格应变，导致竞争的铁磁（FM）-反铁磁（AFM）的相互作用，或有效的自旋挫折，局部自旋之间的发病。当铁被纳入到1.8 nm的ZnSe量子点的磁性计算分析，使用标准密度泛函理论（DFT）模拟，和由此产生的自旋和Fe局域化模型进行实验评估，使用SQUID，57 Fe Mo穆斯堡尔谱，和电子顺磁共振（EPR）光谱。实验结果与DFT预测行为一致的观察结果表明，当目标是所需的材料特性时，使用建模的价值。
Traditionally computational methods have been employed to explain the observation of novel properties in materials. The use of computational models to anticipate the onset of such properties in quantum dots (QDs) a priori of their synthetic preparation would facilitate the rapid development of new materials. We demonstrate that the use of computational modeling can allow the design of magnetic semiconductor QDs based on iron doped ZnSe prior to the preparation of the sample. DFT modeling predicts the formation of multinuclear Fe clusters within the 10% Fe doped ZnSe QD to relieve lattice strain leading to the onset of competing ferromagnetic (FM)–antiferromagnetic (AFM) interactions, or in effect spin frustration, between the local spins. The magnetic properties when iron is incorporated into a 1.8 nm ZnSe QD are computationally analyzed using standard density functional theory (DFT) simulations, and the resultant spin and Fe localization models are experimentally evaluated using SQUID,57Fe Mössbauer, and electron paramagnetic resonance (EPR) spectroscopy. The observation that the experimental results agree with the DFT predicted behavior demonstrates the value of using modeling when targeting a desired material property.