Universität Stuttgart
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Item Open Access Process-based screening of porous materials for vacuum swing adsorption based on 1D classical density functional theory and PC-SAFT(2025) Mayer, Fabian; Buhk, Benedikt; Schilling, Johannes; Rehner, Philipp; Gross, Joachim; Bardow, AndréAdsorption-based processes are showing substantial potential for carbon capture. Due to the vast space of potential solid adsorbents and their influence on the process performance, the choice of the material is not trivial but requires systematic approaches. In particular, the material choice should be based on the performance of the resulting process. In this work, we present a method for the process-based screening of porous materials for pressure and vacuum swing adsorption. The method is based on an equilibrium process model that incorporates one-dimensional classical density functional theory (1D-DFT) and the PC-SAFT equation of state. Thereby, the presented method can efficiently screen databases of potential adsorbents and identify the best-performing materials as well as the corresponding optimized process conditions for a specific carbon capture application. We apply our method to a point-source carbon capture application at a cement plant. The results show that the process model is crucial to evaluating the performance of adsorbents instead of relying solely on material heuristics. Furthermore, we enhance our approach through multi-objective optimization and demonstrate for materials with high performance that our method is able to capture the trade-offs between two process objectives, such as specific work and purity. The presented method thus provides an efficient screening tool for adsorbents to maximize process performance.Item Open Access Development of hydrodynamic density functional theory for mixtures and application to droplet coalescence(Stuttgart : Universität Stuttgart, Institut für Technische Thermodynamik und Thermische Verfahrenstechnik, 2021) Stierle, Rolf; Groß, Joachim (Prof. Dr.-Ing.)Predicting accurately coalescence phenomena is critical to the accurate description of the hydrodynamics of fluids and their mixtures. A promising framework for the development of models for such phenomena is dynamic density functional theory. Dynamic density functional theory enables the analysis of dynamical processes in inhomogeneous systems of pure fluids and fluid mixtures at the molecular level. In this work, a hydrodynamic density functional theory model for mixtures in conjunction with Helmholtz energy functionals based on the PC-SAFT equation of state is proposed, that obeys the first and second law of thermodynamics and simplifies to the isothermal Navier-Stokes equation for homogeneous systems. The hydrodynamic density functional theory model is derived from a variational principle and accounts for both viscous forces and diffusive molecular transport. A Maxwell-Stefan model is applied for molecular transport. This work identifies a suitable expression for the driving force for molecular diffusion of inhomogeneous systems that captures the effect of interfacial tension. The proposed hydrodynamic density functional theory is a non-local theory that requires the computation of weighted (spatial averaged) densities around each considered spatial coordinate by convolution, which is computationally expensive. This work uses Fourier-type transforms to determine the weighted densities. A pedagogical derivation is presented for the efficient computation of the convolution integrals occurring in the Helmholtz energy functionals in Cartesian, cylindrical, and spherical coordinates on equidistant grids using fast Fourier and similar transforms. The applied off-the-shelf algorithms allow to reduce dimensionality and complexity of many practical problems. Furthermore, an algorithm for a fast first-order Hankel transform is proposed, allowing fast and easy density functional theory calculations in rotationally symmetric systems. Application of the hydrodynamic density functional theory model using a well-balanced finite-volume scheme to one-dimensional droplet and bubble coalescence of pure fluids and binary mixtures is presented. The required transport coefficients, shear viscosity and Maxwell-Stefan diffusion coefficients, are obtained by applying entropy scaling to inhomogeneous fluids. The considered systems show a qualitative difference in the coalescence characteristics of droplets compared to bubbles. This constitutes a first step towards predicting the phase rupture leading to coalescence using dynamic density functional theory.Item Open Access A new dispersion contribution based on the PCP-SAFT equation of state in the framework of classical density functional theory(Stuttgart : Universität Stuttgart, Institut für Technische Thermodynamik und Thermische Verfahrenstechnik, 2019) Sauer, Elmar; Groß, Joachim (Prof. Dr.-Ing.)This dissertation presents the development and evaluation of a dispersion contribution model of a Helmholtz energy functional in the framework of classical density functional theory. The model is based on the PCP-SAFT equation of state and was applied to fluid-liquid interfaces, confined systems, and sessile droplet systems.Item Open Access Calculation of pure substance and mixture viscosities using PCP-SAFT and entropy scaling(Stuttgart : Universität Stuttgart, Institut für Technische Thermodynamik und Thermische Verfahrenstechnik, 2020) Lötgering-Lin, Oliver; Gross, Joachim (Prof. Dr.-Ing.)Item Open Access Predicting and rationalizing the Soret coefficient of binary Lennard‐Jones mixtures in the liquid state(2022) Zimmermann, Nils E. R.; Guevara‐Carrion, Gabriela; Vrabec, Jadran; Hansen, NielsThe thermodiffusion behavior of binary Lennard‐Jones mixtures in the liquid state is investigated by combining the individual strengths of non‐equilibrium molecular dynamics (NEMD) and equilibrium molecular dynamics (EMD) simulations. On the one hand, boundary‐driven NEMD simulations are useful to quickly predict Soret coefficients because they are easy to set up and straightforward to analyze. However, careful interpolation is required because the mean temperature in the measurement region does not exactly reach the target temperature. On the other hand, EMD simulations attain the target temperature precisely and yield a multitude of properties that clarify the microscopic origins of Soret coefficient trends. An analysis of the Soret coefficient suggests a straightforward dependence on the thermodynamic properties, whereas its dependence on dynamic properties is far more complex. Furthermore, a limit of applicability of a popular theoretical model, which mainly relies on thermodynamic data, was identified by virtue of an uncertainty analysis in conjunction with efficient empirical Soret coefficient predictions, which rely on model parameters instead of simulation output. Finally, the present study underscores that a combination of predictive models and EMD and NEMD simulations is a powerful approach to shed light onto the thermodiffusion behavior of liquid mixtures.Item Open Access Optimization of transferable force fields based on reduced order and surrogate models(Stuttgart : Universität Stuttgart, Institut für Technische Thermodynamik und Thermische Verfahrenstechnik, 2025) Fleck, Maximilian; Hansen, Niels (apl. Prof. Dr.-Ing. habil.)Classical transferable force fields are utilized to make predictions about physical properties and microscopic processes in both pure substances and mixtures. The process of developing these force fields involves optimizing the nonbonded interactions through multidimensional optimization techniques, utilizing computationally intensive Monte Carlo and molecular dynamics simulations. Due to the high correlation of force field parameters, this optimization process can be challenging. To improve efficiency, physically-based surrogate models were utilized to expedite the optimization process. In particular, new reduced order models for transport properties based on entropy scaling were developed. Now transport properties such as viscosity, thermal conductivity and self-diffusion coefficients can be represented in the optimization of force fields through surrogate models alongside static properties, which was previously not possible. Furthermore, machine-learned models for transport properties based on linear multifidelity Gaussian processes utilized in reduced residual entropy space were developed. Here, experimental data is combined with simulation results which enables to enhance predictive capabilities and optimize force fields more effectively.Item Open Access Generalized DeepONets for viscosity prediction using learned entropy scaling references(2025) Fleck, Maximilian; Spera, Marcelle B. M.; Darouich, Samir; Klenk, Timo; Hansen, NielsData-driven approaches used to predict thermophysical properties benefit from physical constraints because the extrapolation behavior can be improved and the amount of training data be reduced. In the present work, the well-established entropy scaling approach is incorporated into a neural network architecture to predict the shear viscosity of a diverse set of pure fluids over a large temperature and pressure range. Instead of imposing a particular form of the reference entropy and reference shear viscosity, these properties are learned. The resulting architecture can be interpreted as two linked DeepONets with generalization capabilities.Item Open Access Data management and analysis of metal : organic framework synthesis using data models(2026) Neubauer, Felix; Endo, Kenichi; Bender, Frederic; Ciftci, Esengül; Hansen, Niels; Krause, Simon; Uekermann, Benjamin; Pleiss, JürgenThe successful synthesis of metal-organic frameworks (MOFs) in high yield and purity critically depends on the details of the procedure. Therefore, the machine-readable as well as findable, accessible, interoperable, and reusable (FAIR) documentation of the synthesis procedure and the associated characterization data is crucial to ensure reproducibility and to enable data-driven analysis and systematic optimization of synthesis. Here, we demonstrate a data-processing workflow developed based on a JSON Schema data model for the synthesis and characterization of MOFs. Its feasibility and usefulness are demonstrated by synthesis data of two MOF systems, Fe-terephthalate MOF and MOCOF-1, and their subsequent characterization by powder X-ray diffraction (PXRD). The data model supports the development of an integrated workflow to (1) parse synthesis data from a table or an electronic lab notebook (ELN) into standardized JSON forms, (2) validate the data sets for errors and incompleteness, (3) serialize the data into the standardized data exchange formats MPIF and XDL, and (4) analyze PXRD data by a decision tree to identify critical synthesis parameters that control phase selectivity and yield. The data model and the workflow are modular and extensible and can be adapted to other data sources, characterizations, and AI methods for analysis. The proposed data model strategy makes MOF synthesis FAIR and AI-ready, fosters the digitalization of synthetic chemistry, and accelerates discovery.Item Open Access Efficient prediction of multicomponent adsorption isotherms and enthalpies of adsorption in MOFs using classical density functional theory(2026) Thiele, Nadine; Teh, Tiong Wei; Bursik, Benjamin; Granderath, Marcel; Bauer, Gernot; Dufour-Décieux, Vincent; Rehner, Philipp; Stierle, Rolf; Bardow, André; Hansen, Niels; Gross, JoachimWe demonstrate that classical density functional theory (DFT) based on the PC-SAFT equation of state is a fast, accurate, and predictive model to predict multicomponent adsorption in porous materials, which is an essential step toward the design of next-generation adsorbents for relevant applications. Using GPU acceleration, adsorption isotherms and adsorption enthalpies can be obtained in a matter of seconds, which is several orders of magnitude faster than grand canonical Monte Carlo (GCMC) simulations. Using metal-organic frameworks as adsorbents and non- or weakly polar molecules as adsorbates, we validate our approach by performing GCMC simulations for binary, ternary, and quaternary mixtures with practically relevant applications, such as noble gas separations (Kr/Xe, Ar/Kr/Xe), direct dry air capture (CO2/N2), hydrogen enrichment (CH4/H2, CH4/H2/N2) and adsorbed natural gas (CH4/C3H8, CH4/C2H6/C3H8, CH4/C2H6/C3H8/N2). Classical DFT reproduces loadings and adsorption enthalpies of the mixtures in close agreement with results from GCMC simulations. Thus, classical DFT expands our toolbox for studying multicomponent adsorption.Item Open Access Interfacial properties using classical density functional theory : curved interfaces and surfactants(Stuttgart : Universität Stuttgart, Institut für Technische Thermodynamik und Thermische Verfahrenstechnik, 2021) Rehner, Philipp; Groß, Joachim (Prof. Dr.-Ing.)Interfaces play an important role in natural and industrial processes. Classical density functional theory (DFT) has been established as a tool capable of predicting interfacial properties, but also of providing insight in the structure of fluids at interfaces. Compared to other statistical mechanical methods, particularly molecular simulation, an efficient implementation of DFT offers a significant reduction in computation time. This advantage comes with the cost of an increased modeling effort. In this work, the calculation of interfacial properties using DFT is discussed and applied to different aspects of interfaces. First, the properties of highly curved interfaces, as they appear in nucleation processes, are studied. This is done first by directly calculating the properties of nanodroplets using DFT in spherical coordinates and afterwards in an expansion around a flat interface. Because for some applications, the calculation time of DFT is a limiting factor, a new method to predict surface tensions from equation of state parameters is introduced. This is achieved by using a Taylor expansion of the full DFT Helmholtz energy functional around a local density. The resulting functional is identical to that used in density gradient theory except for an explicit, temperature and density dependent expression for the influence matrix. The method is subsequently used to examine in detail the parametrization of associating components, particularly water and alcohols, that pose difficulties with respect to the simultaneous description of bulk phase equilibria and interfacial properties. A multiobjective optimization approach is used to assess different models and to quantify their capabilities and limitations. The so obtained water model presents the foundation for the last segment of this work, that studies the interfacial properties of water/surfactant and water/alkane/surfactant systems. The amphiphilic surfactant molecules are modeled using a heteronuclear DFT approach that resolves the distributions of individual segments. The parameters of this group contribution method are obtained by fitting to properties of small surfactant molecules and can then be used to predict properties of larger molecules for which less or no experimental data is available. The model is used to study the adsorption and orientation of surfactant molecules at interfaces and the corresponding reduction in interfacial tension.