A COSMO‐RS based QSPR model for the lubricity of biodiesel and petrodiesel components

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

Novel global and local 3D atom-based linear descriptors of the Minkowski distance matrix: theory, diversity–variability analysis and QSPR applications

“…The total and local non-stochastic, stochastic and doubly-stochastic 3D molecular linear descriptors on Minkowski atom-atom distance matrices [Eqs. (16)- (18)] were codified in an experimental version of TOMOCOMD-CARDD software (QuBiLs-MiDAS module) and calculated for the datasets mentioned above. In this work, the norm (n) and step-count (p) were ranged between 1-3 and 1-15, respectively.…”

“…Although 0D-2D molecular descriptors (MDs) are routinely applied in endpoint predictions, many properties have been shown to require more detailed 3D information to properly capture the relevant structural features responsible for the description or modeling of endpoints of interest [14][15][16][17][18][19][20][21][22]. It has become evident that physical, chemical, or biological properties of a compound depend on the three-dimensional (3D) arrangements of the atoms in the molecule.…”

Lubricity of new Tailor-Made Fuels from Biomass

“…Both DIN EN ISO 12156-1 and ASTM D6079 use the averaged wear scar diameter of the ball to determine the fluid's lubricity. However Fatemimoughari et al have reported that for substances with very good lubricating abilities no measurable wear could be found on the ball [8].…”

“…Consequently these were screened. To gain a more detailed understanding of the properties influencing a fuels capability to reduce wear and friction in the HFRR several additional substances were also tested ( [2], [8]). In Figure 7 some of these early results are shown.…”

Factors affecting bubble size in ionic liquids

“…More interestingly, in contrast to what has been observed for the two first reported QSPR approaches, the variation of the mean bubble size with the respect to ILs structure is more correctly evaluated by using this third approach as the mean bubble size follows this order: nor the correct trend for investigated halide-based ILs, the combination of ILs sigma profiles, viscosity and contact angle provides a more quantitative correlation than the approaches reported previously. As claimed by Klamt et al 23,40,41 and demonstrated by several authors, 42,43 QSPR models could be developed by using solely sigma moments, which are directly determined by the COSMOthermX software. In this case, a given property (P) could be correlated by multilinear regression of the sigma moments as follows:…”