Computational prediction of limiting activity coefficients is of great relevance for process
design. For highly nonideal mixtures including molecules with directed interactions, methods …

Digitalization is about data and how they are used. This has always been a key topic in
applied thermodynamics. In the present work, the influence of the current wave of …

We present a generic way to hybridize physical and data-driven methods for predicting
physicochemical properties. The approach 'distills' the physical method's predictions into a …

Nuclear magnetic resonance (NMR) spectroscopy is a powerful tool for qualitative and
quantitative analysis. However, for complex mixtures, determining the speciation from NMR …

Predicting molecular interactions is a crucial step for chemical process modeling. It requires
the full knowledge of the analyzed system, however, this is often impossible in complex real …

Poorly specified mixtures, whose composition is unknown, are ubiquitous in chemical and
biochemical engineering. In the present work, we propose a rational method for defining and …

In many chemical engineering processes, mixtures occur whose composition is not well-
known. Simulations of processes with such poorly specified mixtures are basically …

Nuclear magnetic resonance (NMR) spectroscopy is a powerful tool for elucidating the
structure of unknown components and the composition of liquid mixtures. However, these …

In the framework of the binary quantum cluster equilibrium theory, we introduce a cluster
approach to access activity coefficients of binary mixtures. This approach allows derivation …

The influence of various factors (eg, temperature, stirring speed, pH, and alginic acid
concentration) on the crystallization kinetics of magnesium ammonium phosphate …