Oxidation kinetics of nickel nano crystallites obtained by controlled thermolysis of diaquabis(ethylenediamine) nickel(II) nitrate

The metal complex, [Ni(en)2(H2O)2](NO3)2 (en = ethylenediamine), was decomposed in a static furnace at 200 C by autogenous decomposition to obtain phase pure metallic nickel nanocrystallites. The nickel metal thus obtained was studied by XRD, IR spectra, SEM and CHN analysis. The nickel crystallites are in the nanometer range as indicated by XRD studies. The IR spectral studies and CHN analyses show that the surface is covered with a nitrogen containing species. Thermogravimetric mass gain shows that the product purity is high (93%). The formed nickel is stable and resistant to oxidation up to 350 C probably due to the coverage of nitrogen containing species. Activation energy for the oxidation of the prepared nickel nanocrystallites was determined by non-isothermal methods and was found to depend on the conversion ratio. The oxidation kinetics of the nickel crystallites obeyed a Johnson–Mehl–Avrami mechanism probably due to the special morphology and crystallite strain present on the metal.

Nonparametric Estimation of Survivor Function in Bivariate Competing Risk Models’

So far, in the bivariate set up, the analysis of lifetime (failure time) data with multiple causes of failure is done by treating each cause of failure separately. with failures from other causes considered as independent censoring. This approach is unrealistic in many situations. For example, in the analysis of mortality data on married couples one would be interested to compare the hazards for the same cause of death as well as to check whether death due to one cause is more important for the partners’ risk of death from other causes. In reliability analysis. one often has systems with more than one component and many systems. subsystems and components have more than one cause of failure. Design of high-reliability systems generally requires that the individual system components have extremely high reliability even after long periods of time. Knowledge of the failure behaviour of a component can lead to savings in its cost of production and maintenance and. in some cases, to the preservation of human life. For the purpose of improving reliability. it is necessary to identify the cause of failure down to the component level. By treating each cause of failure separately with failures from other causes considered as independent censoring, the analysis of lifetime data would be incomplete. Motivated by this. we introduce a new approach for the analysis of bivariate competing risk data using the bivariate vector hazard rate of Johnson and Kotz (1975).