Marcin Wladyslaw Wolter

Diffusion Rates of51Cr,54Mn and 59Fe in MnCr2O4 and FeCr2O4 Spinels

As the result of oxidation of Cr-Mn steels in SO2 the three layer scale is formed. The intermediate layer of this scale is composed mainly of MnCr2O4 spinel whereas FeCr2O4 spinel is present in small amount. MnO dominates in the outer layer. The inner, very thin scale layer contains oxides/sulfides mixture. The aim of this study was to examine self-diffusion processes in both spinels by multitracer method of diffusion measurements to know which of the transport processes during oxidation is the smallest one and deciding on the corrosion rate. In diffusion experiments the radioisotopes 54Mn, 51Cr and 59Fe were used. The serial sectioning method was applied to simultaneous evaluation of diffusion rates of chromium, manganese and iron in both spinels at 1073 K and 1173 K under the pressure of 105 Pa in SO2 containing 10 Pa O2. These spinels were obtained by modified sol-gel method from nitrates. Structures of the spinels were examined by X-ray spectrometry. It was found, that the diffusion rates of metals are higher in MnCr2O4 spinel. Moreover the dominant mechanism of manganese transport (the highest one) in studied samples is the volume diffusion while chromium and iron are transported mainly through the high diffusivity paths.

Tracer diffusion of iron in α-Mn1±yS polycrystals

Abstract Tracer diffusion of iron in polycrystalline samples of α-MnS was studied. In the diffusion experiments the radioactive isotope of iron, 59 Fe, was used. The dependence of diffusion coefficient on sulfur activity and temperature was investigated. It was found that the dominant transport mechanism is the volume diffusion, which proceeds via point defects of α-MnS, and that the iron diffusion rate is larger than the self-diffusion of manganese in manganous sulfide. The activation energy for the vacancy mechanism of diffusion was calculated.

OPTIMIZATION OF TAU IDENTIFICATION IN ATLAS EXPERIMENT USING MULTIVARIATE TOOLS

Elementary particle physics experiments, which search for very rare processes, require the efficient analysis and selection algorithms able to separate a signal from the overwhelming background. Four learning machine algorithms have been applied to identify τ leptons in the ATLAS experiment: projective likelihood estimator (LL), Probability Density Estimator with Range Searches (PDE-RS), Neural Network, and the Support Vector Machine (SVM). All four methods have similar performance, which is significantly better than the baseline cut analysis. This indicates that the achieved background rejection is close to the maximal achievable performance.