Increasing carbon dioxide accumulation in earth's atmosphere and the depletion of fossil resources pose huge challenges for our society and, in particular, for all stakeholders in the transportation sector. The Cluster of Excellence 'Tailor-Made Fuels from Biomass' at RWTH Aachen University establishes innovative and sustainable processes for the conversion of whole plants into molecularly well-defined fuels exhibiting tailored properties for low-temperature combustion engine processes, enabling high efficiency and low pollutant emissions. The concept of fuel design, that is, considering fuel's molecular structure to be a design degree of freedom, aims for the simultaneous optimisation of fuel production and combustion systems. In the present contribution, three examples of tailor-made biofuels are presented. For spark ignition engines, both 2-methylfuran and 2-butanone show increased knock resistance compared to RON95 gasoline, thus enabling a higher compression ratio and an efficiency gain of up to 20% at full-load operation. Moreover, both fuels comprise a good mixture formation superior to the one of ethanol, especially under difficult boundary conditions. For compression ignition engines, 1-octanol enables a remarkable reduction in engine-out soot emissions compared to standard diesel fuel due to the high oxygen content and lower reactivity. This advantage is achieved without sacrificing the high indicated efficiency and low NO X emissions.

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Quantitative structure-property relations (QSPR) employing descriptors derived from the three-dimensional (3D) molecular structure are frequently applied for property prediction in various fields of research. However, there is no common understanding of the necessary degree of detail to which molecular structure has to be known for reliable descriptor evaluation, but computational methods used vary from simplified molecular mechanics up to rigorous ab initio programs. In order to quantify the yet unknown error due to this heterogeneity, widely used 3D molecular descriptors from diverse fields of application are evaluated for molecular structures computed by different computational methods. The results clearly indicate that the widespread, exclusive use of the most stable molecular conformation as well as too simplistic computational methods yield systematically erroneous descriptor values with misleading information for the inferred structure-property relations. Thus, generating an awareness and understanding of this fundamental problem is considered an important first step to make 3D QSPR a generally accepted property prediction method.