Thermodynamic functions of element 105 in neutral and ionized states

The basic thermodynamic functions, the entropy, free energy, and enthalpy, for element 105 (hahnium) in electronic configurations d^3 s^2, d^3 sp, and d^4s^1 and for its +5 ionized state (5f^14) have been calculated as a function of temperature. The data are based on the results of the calculations of the corresponding electronic states of element 105 using the multiconfiguration Dirac-Fock method.

Predicted properties of the superheavy elements. III. Element 115, Eka-Bismuth

Element 115 is expected to be in group V-a of the periodic table and have most stable oxidation states of I and III. The oxidation state of I, which plays a minor role in bismuth chemistry, should be a major factor in 115 chemistry. This change will arise because of the large relativistic splitting of the spherically symmetric 7p_l/2 shell from the 7P_3/2 shell. Element 115 will therefore have a single 7p_3/2 electron outside a 7p^2_1/2 closed shell. The magnitude of the first ionization energy and ionic radius suggest a chemistry similar to Tl^+. Similar considerations suggest that 115^3+ will have a chemistry similar to Bi^3+. Hydrolysis will therefore be easy and relatively strongly complexing anions of strong acids will be needed in general to effect studies of complexation chemistry. Some other properties of 115 predicted are as follows: ionization potentials I 5.2 eV, II 18.1 eV, III 27.4 eV, IV 48.5 eV, 0 \rightarrow 5^+ 159 eV; heat of sublimation, 34 kcal (g-atom)^-1; atomic radius, 2.0 A; ionic radius, 115^+ 1.5 A, 115^3+ 1.0 A; entropy, 16 cal deg^-1 (g-atom)^-l (25°); standard electrode potential 115^+ |115, -1.5 V; melting and boiling points are similar to element 113.

On Solution Sets of Information Inequalities

We investigate solution sets of a special kind of linear inequality systems. In particular, we derive characterizations of these sets in terms of minimal solution sets. The studied inequalities emerge as information inequalities in the context of Bayesian networks. This allows to deduce important properties of Bayesian networks, which is important within causal inference.