A procedure for the synthesis of tertiary amines via reductive amination of aldehydes with molecular hydrogen as a reducing agent using homogeneous rhodium catalysis is presented. Using an amine to aldehyde ratio of 4/1 enabled the synthesis of tertiary amines from nine different aldehydes and nine different secondary amines with selectivities up to 99% and turnover frequencies (TOF) up to 7200 h À 1. The reaction showed a high tolerance against alcohol and ester functions allowing the formation of multifunctional molecules. In addition, secondary amines can also be produced by this synthesis. For all compounds, activities were determined by hydrogen gas-uptake. In order to increase the sustainability and efficiency of the procedure, a dosing strategy has been successfully developed. Using the determined reaction indicators enabled the stoichiometric use of aldehydes and amines without significant loss of selectivity.

Optimization-based design tools for energy systems often require a large set of parameter assumptions, e.g., about technology efficiencies and costs or the temporal availability of variable renewable energies. Understanding the influence of all these parameters on the computed energy system design via direct sensitivity analysis is not easy for human decision-makers, since they may become overloaded by the multitude of possible results. We thus propose transferring an approach from explaining complex neural networks, so-called locally interpretable model-agnostic explanations (LIME), to this related problem. Specifically, we use variations of a small number of interpretable, high-level parameter features and sparse linear regression to obtain the most important local explanations for a selected design quantity. For a small bottom-up optimization model of a grid-connected building with photovoltaics, we derive intuitive explanations for the optimal battery capacity in terms of different cloud characteristics. For a larger application, namely a national model of the German energy transition until 2050, we relate path dependencies of the electrification of the heating and transport sector to the correlation measures between renewables and thermal loads. Compared to direct sensitivity analysis, the derived explanations are more compact and robust and thus more interpretable for human decision-makers.

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