Performance Evaluation and Experimental Studies on Metallised Gel Propellants

Metallised gel propellants offer higher specific impulse and volumetric loading, reduced vaporisation loss, spillage and slosh problems and easy storage in comparison to the conventional liquid propellants. Theoretical performance analysis of gel propellant containing Al in unsymmetrical dimethyl hydrazine-dinitrogen tetroxide (UDMH-N/sub 2/O/sub 4} system shows peak Isp (vacuum condition) of 316.7 s and 318.3 s at oxidiser/fuel (O/f) ratios of 1.5 and 1.0, respectively for 30 per cent and 40 per cent UDMH-Al gel propellants, under standard conditions. The effect of other parameters like area ratio and chamber pressure on performance has been brought out in view of mission oriented applications. Aluminium has been found to be a better choice over magnesium in metallised gel propellants. Experimental studies on UDMH gellation using propellant grade (15 micrometer)and pyrotechnic grade (1.5 micrometer)Al in 500g batch level show that gellant(methyl cellulose) concentration could be reduced by 50 percent using pyrotechnic grade Al. The pseudoplastic-thixotropic behaviour, flow rate through die holes, burst pressure tests and bulk density are studied. UDMH -25 to 30 per cent Al gels with both grades of Al are found to be stable, pseudoplastic (shear thinning) and thixotropic (time-dependent shear thinning), but their flow pattern through die holes differ in nature

Rapid and Accurate Prediction and Scoring of Water Molecules in Protein Binding Sites

Water plays a critical role in ligand-protein interactions. However, it is still challenging to predict accurately not only where water molecules prefer to bind, but also which of those water molecules might be displaceable. The latter is often seen as a route to optimizing affinity of potential drug candidates. Using a protocol we call WaterDock, we show that the freely available AutoDock Vina tool can be used to predict accurately the binding sites of water molecules. WaterDock was validated using data from X-ray crystallography, neutron diffraction and molecular dynamics simulations and correctly predicted 97% of the water molecules in the test set. In addition, we combined data-mining, heuristic and machine learning techniques to develop probabilistic water molecule classifiers. When applied to WaterDock predictions in the Astex Diverse Set of protein ligand complexes, we could identify whether a water molecule was conserved or displaced to an accuracy of 75%. A second model predicted whether water molecules were displaced by polar groups or by non-polar groups to an accuracy of 80%. These results should prove useful for anyone wishing to undertake rational design of new compounds where the displacement of water molecules is being considered as a route to improved affinity