No abstract

The development of a new, optimised fuel is a process in which various necessary fuel properties have to be taken into account. For an inclusion of candidates not synthesised yet into the design process, a fully predictive model relying on nothing but the molecular structure is mandatory for each relevant property. One of the most important aspects for the design of a fuel is its lubricity. In this study, the predictive method conductor-like screening model for realistic solvation (COSMO-RS) for the calculation of thermodynamic mixture properties is adopted for deriving a quantitative structure-property relationship for the lubricity of fuel components. COSMO-RS calculates molecular descriptors (sigma moments) based on quantum chemical calculations. These descriptors are adapted to describe the underlying phenomena causing the film formation ability and lubricity of the fuel. The lubricity is assessed via high-frequency reciprocating rig measurements taken from literature. The molecular descriptors and experimental data are evaluated via statistical methods in order to find the most influential molecular descriptors. ABBREVIATIONS BP-RI-DFT Becke-Perdew resolution of identity density functional theory COSMO conductor-like screening model COSMO-RS conductor-like screening model for realistic solvation HFRR high-frequency reciprocating rig HOMO highest occupied molecular orbital LUMO lowest unoccupied molecular orbital RMSECV root-mean-square error of leave-one-out cross validation SSCD surface screening charge distribution

No abstract

9 ProcessNet -Biotechnologen 1203