Using the sol-gel method we synthesized hematite (α − Fe2O3) nanoparticles in a silica matrix with 60 wt % of hematite. X-ray diffraction (XRD) patterns and Fourier transform infrared (FTIR) spectra of the sample demonstrate the formation of the α − Fe2O3 phase and amorphous silica. A transmission electron microscopy (TEM) measurements show that the sample consists of two particle size distributions of the hematite nanoparticles with average sizes around 10 nm and 20 nm, respectively. Magnetic properties of hematite nanoparticles were measured using a superconducting quantum interference device (SQUID). Investigation of the magnetic properties of hematite nanoparticles showed a divergence between field-cooled (FC) and zero-field-cooled (ZFC) magnetization curves and two maxima. The ZFC magnetization curves displayed a maximum at around TB = 50 K (blocking temperature) and at TM = 83 K (the Morin transition). The hysteresis loop measured at 5 K was symmetric around the origin, with the values of coercivity, remanent and mass saturation magnetization HC10K ≈ 646 A/cm, (810 Oe), Mr10K = 1.34 emu/g and MS10K = 6.1 emu/g respectively. The absence of both coercivity (HC300K = 0) and remanent magnetization (Mr300K = 0) in M(H) curve at 300 K reveals super-paramagnetic behavior, which is desirable for application in biomedicine. The bimodal particle size distributions were used to describe observed magnetic properties of hematite nanoparticles. The size distribution directly influences the magnetic properties of the sample.
Production planning in an underground mine plays a key activity in the mining company business. It is supported by the fact that mineral industry is unique and volatile environment. There are two uncertain parameters that cannot be managed by planners, metal price, and operating costs. Having ability to quantify and incorporate them in the process of planning can help companies to do their business in much easier way. We quantify these uncertainties by the simulation of mean reverting process and Itô-Doob stochastic differential equation, respectively. Mineral deposit is represented as a set of mineable blocks and room and pillar mining method is selected as a way of mining. Multicriteria clustering algorithm is used to create areas inside of mineral deposit that have technological characteristics required by the planners. We also developed a way to forecast the volatility of economic values of these areas through the planning period. Fuzzy 0-1 linear programming model is used to define the sequence of mining of these areas by maximization of the expected value of the fuzzy future cash flow. Model was tested on small hypothetical lead-zinc mineral deposit and results showed that our approach was able to solve such complex problem.
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