Tunable Perpendicular Magnetic Anisotropy in Off-Stoichiometric Full-Heusler Alloy Co_2MnAl

Off-stoichiometric full-Heusler alloy Co_2 MnAl thin films with different thicknesses are epitaxially grown on GaAs(001) substrates by molecular-beam epitaxy. The composition of the films, close to Co_(1.65)Mn_(1.35)Al(CMA),is determined by x-ray photoelectron spectroscopy and energy dispersive spectroscopy. Tunable perpendicular magnetic anisotropy(PMA) from 3.41 Merg/cm^3 to 1.88 Merg/cm^3 with the thickness increasing from 10 nm to 30 nm is found,attributed to the relaxation of residual compressive strain. Moreover, comparing with the ultrathin CoFeB/MgO used in the conventional perpendicular magnetic tunnel junction, the CMA electrode has a higher magnetic thermal stability with more volume involved. The PMA in CMA films is sustainable up to 300℃,compatible with semiconductor techniques. This work provides a possibility for the development of perpendicular magnetized full-Heusler compounds with high thermal stability and spin polarization.

Epitaxial Growth and Magnetic Properties of NiMnAs Films on GaAs Substrates

Single-phase Ni_(0.92)Mn_(1.08) As films with strained C_(1b) symmetry are grown on GaAs(001) substrates. In addition,a preferred epitaxial configuration of(110)-orientated Ni_(0.92)Mn_(1.08) As on(001)-orientated GaAs is revealed by synchrotron radiation measurement. The magnetic properties of the films are found to be significantly influenced by the growth temperature and the optimized growth temperature is determined to be ~370℃. According to the results of x-ray absorption spectroscopy, these phenomena can be attributed to the variation of the local electronic structure of the Mn atoms. Our work provides useful information for the further investigations of NiMnAs, which is a theoretically predicted half-metal.

Dynamic forecasting and optimal scheduling of by-product gases in integrated iron and steel works

The by-product gases, which are generated in ironmaking, coking and steel making processes, can be used as fuel for the metallurgical processes and on-site power plants. However, if the supply and demand of by-product gases are imbalanced, gas flaring may occur, leading to energy wastage and environmental pollution. Therefore, optimal scheduling of by-product gases is important in iron and steel works. A BP_LSSVM model, which combines back-propagation (BP) neural network and least squares support vector machine (LSSVM), and an improved mixed integer linear programming model were proposed to forecast the surplus gases and allocate them optimally. To maximize energy utilization, the stability of gas holders and boilers was considered and a concise heuristic procedure was proposed to assign penalties for boilers and gas holders. Moreover, the optimal level of gas holder was studied to enhance the stability of the gas system. Compared to the manual operation, the optimal results showed that the electricity generated by the power plant increased by 2.93% in normal condition and by 22.2% in overhaul condition. The proposed model minimizes the total cost by optimizing the boiler load with less adjustment frequency and the stability of gas holders and can be used as a guidance in dynamic forecasting and optimal scheduling of by-product gases in integrated iron and steel works.