Elucidating the behavior of the SARS-CoV-2 virus surface at vapor-liquid interfaces using molecular dynamics simulation.

Aerosols play a major role in the transmission of the SARS-CoV-2 virus. The behavior of the virus within aerosols is therefore of fundamental importance. On the surface of a SARS-CoV-2 virus, there are about 40 spike proteins, which each have a length of about 20 nm. They are glycosylated trimers, which are highly flexible, due to their structure. These spike proteins play a central role in the intrusion of the virus into human host cells and are, therefore, a focus of vaccine development. In this work, we have studied the behavior of spike proteins of the SARS-CoV-2 virus in the presence of a vapor-liquid interface by molecular dynamics (MD) simulations. Systematically, the behavior of the spike protein at different distances to a vapor-liquid interface were studied. The results reveal that the spike protein of the SARS-CoV-2 virus is repelled from the vapor-liquid interface and has a strong affinity to stay inside the bulk liquid phase. Therefore, the spike protein bends when a vapor-liquid interface approaches the top of the protein. This has important consequences for understanding the behavior of the virus during the dry-out of aerosol droplets.

Automated nuclear magnetic resonance fingerprinting of mixtures.

Nuclear magnetic resonance (NMR) spectroscopy is a powerful tool for qualitative and quantitative analysis. However, for complex mixtures, determining the speciation from NMR spectra can be tedious and sometimes even unfeasible. On the other hand, identifying and quantifying structural groups in a mixture from NMR spectra is much easier than doing the same for components. We call this group-based approach "NMR fingerprinting." In this work, we show that NMR fingerprinting can even be performed in an automated way, without expert knowledge, based only on standard NMR spectra, namely, 13C, 1H, and 13C DEPT NMR spectra. Our approach is based on the machine-learning method of support vector classification (SVC), which was trained here on thousands of labeled pure-component NMR spectra from open-source data banks. We demonstrate the applicability of the automated NMR fingerprinting using test mixtures, of which spectra were taken using a simple benchtop NMR spectrometer. The results from the NMR fingerprinting agree remarkably well with the ground truth, which was known from the gravimetric preparation of the samples. To facilitate the application of the method, we provide an interactive website (https://nmr-fingerprinting.de), where spectral information can be uploaded and which returns the NMR fingerprint. The NMR fingerprinting can be used in many ways, for example, for process monitoring or thermodynamic modeling using group-contribution methods-or simply as a first step in species analysis.

Determination of self-diffusion coefficients in mixtures with benchtop 13C NMR spectroscopy via polarization transfer.

Nuclear magnetic resonance (NMR) is an established method to determine self-diffusion coefficients in liquids with high precision. The development of benchtop NMR spectrometers makes the method accessible to a wider community. In most cases, 1H NMR spectroscopy is used to determine self-diffusion coefficients due to its high sensitivity. However, especially when using benchtop NMR spectrometers for the investigation of complex mixtures, the signals in 1H NMR spectra can overlap, hindering the precise determination of self-diffusion coefficients. In 13C NMR spectroscopy, the signals of different compounds are generally well resolved. However, the sensitivity of 13C NMR is significantly lower than that of 1H NMR spectroscopy leading to very long measurement times, which makes diffusion coefficient measurements based on 13C NMR practically infeasible with benchtop NMR spectrometers. To circumvent this problem, we have combined two known pulse sequences, one for polarization transfer from 1H to the 13C nuclei (PENDANT) and one for the measurement of diffusion coefficients (PFG). The new method (PENPFG) was used to measure the self-diffusion coefficients of three pure solvents (acetonitrile, ethanol and 1-propanol) as well as in all their binary mixtures and the ternary mixture at various compositions. For comparison, also measurements of the same systems were carried out with a standard PFG-NMR routine on a high-field NMR instrument. The results are in good agreement and show that PENPFG is a useful tool for the measurement of the absolute value of the self-diffusion coefficients in complex liquid mixtures with benchtop NMR spectrometers.

Polarization transfer methods for quantitative analysis of flowing mixtures with benchtop 13C NMR spectroscopy.

Benchtop NMR spectroscopy is attractive for process monitoring; however, there are still drawbacks that often hamper its use, namely, the comparatively low spectral resolution in 1H NMR, as well as the low signal intensities and problems with the premagnetization of flowing samples in 13C NMR. We show here that all these problems can be overcome by using 1H-13C polarization transfer methods. Two ternary test mixtures (one with overlapping peaks in the 1H NMR spectrum and one with well-separated peaks, which was used as a reference) were studied with a 1 T benchtop NMR spectrometer using the polarization transfer sequence PENDANT (polarization enhancement that is nurtured during attached nucleus testing). The mixtures were analyzed quantitatively in stationary as well as in flow experiments by PENDANT enhanced 13C NMR experiments, and the results were compared with those from the gravimetric sample preparation and from standard 1H and 13C NMR spectroscopy. Furthermore, as a proxy for a process monitoring application, continuous dilution experiments were carried out, and the composition of the mixture was monitored in a flow setup by 13C NMR benchtop spectroscopy with PENDANT. The results demonstrate the high potential of polarization transfer methods for applications in quantitative process analysis with benchtop NMR instruments, in particular with flowing samples.

A child of prediction. On the History, Ontology, and Computation of the Lennard-Jonesium.

The Lennard-Jones (LJ) fluid, named after mathematician-physicist-chemist Sir John Lennard-Jones (1894-1954), occupies a special place among fluids. It is an ideal entity, defined as the fluid whose particles interact according to the Lennard-Jones potential. This paper expounds the history of the LJ fluid to throw light on the tensions between theory and computational practice. The paper argues for the following claims. Firstly, the computational approach-even prior to the computer-pragmatically aims at prediction, not truth. Secondly, computer simulation methods, especially "molecular dynamics" (MD), triggered a change in epistemology. Now, simulated model fluids became targets of investigation in their own right. The urge for prediction turned the LJ fluid into the most investigated fluid in engineering thermodynamics. Thirdly, MD took a huge upswing in the 1990s, due to exploratory options in simulation. We discuss how, under these conditions, predictive success might be fraught with problems of reproducibility.

Extension of the MolMod Database to Transferable Force Fields.

MolMod, a web-based database for classical force fields for molecular simulations of fluids [Mol. Sim. 45, 10 (2019), 806-814], was extended to transferable force fields. Eight transferable force fields, including all-atom and united-atom type force fields, were implemented in the MolMod database: OPLS-UA, OPLS-AA, COMPASS, CHARMM, GROMOS, TraPPE, Potoff, and TAMie. These transferable force fields cover a large variety of chemical substance classes. The system is designed such that new transferable force fields can be readily integrated. A graphical user interface was implemented that enables the construction of molecules. The MolMod database compiles the force field for the specified component and force field type and provides the corresponding data and meta data as well as ready-to-use input files for the molecule for different simulation engines. This helps the user to flexibly choose molecular models and integrate them swiftly in their individual workflows, reducing risks of input errors in molecular simulations.

Molecular dynamics simulation study on the mass transfer across vapor-liquid interfaces in azeotropic mixtures.

Mass transfer through fluid interfaces is an important phenomenon in industrial applications as well as in naturally occurring processes. In this work, we investigate the mass transfer across vapor-liquid interfaces in binary mixtures using molecular dynamics simulations. We investigate the influence of interfacial properties on mass transfer by studying three binary azeotropic mixtures known to have different interfacial behaviors. Emphasis is placed on the effect of the intermolecular interactions by choosing mixtures with the same pure components but different cross-interactions such that different azeotropic behaviors are obtained. The molar flux is created by utilizing a non-stationary molecular dynamics simulation approach, where particles of one component are inserted into the vapor phase over a short period of time before the system's response to this insertion is monitored. From a direct comparison of the density profiles and the flux profiles in close proximity to the interface, we analyze the particles' tendency to accumulate in the interfacial region throughout the different stages of the simulation. We find that for mixtures with strong attractive cross-interactions, the inserted particles are efficiently transported into the liquid phase. For systems with weak attractive cross-interactions, the inserted particles show a tendency to accumulate in the interfacial region, and the flux through the system is lower. The results from this work indicate that the accumulation of particles at the interface can act as a hindrance to mass transfer, which has practical relevance in technical processes.

Molecular modeling and simulation of aqueous solutions of alkali nitrates.

A set of molecular models for the alkali nitrates (LiNO3, NaNO3, KNO3, RbNO3, and CsNO3) in aqueous solutions is presented and used for predicting the thermophysical properties of these solutions with molecular dynamics simulations. The set of models is obtained from a combination of a model for the nitrate anion from the literature with a set of models for the alkali cations developed in previous works of our group. The water model is SPC/E and the Lorentz-Berthelot combining rules are used for describing the unlike interactions. This combination is shown to yield fair predictions of thermophysical and structural properties of the studied aqueous solutions, namely the density, the water activity and the mean ionic activity coefficient, the self-diffusion coefficients of the ions, and radial distribution functions, which were studied at 298 K and 1 bar; except for the density of the solutions of all five nitrates and the activity properties of solutions of NaNO3, which were also studied at 333 K. For calculating the water the activity and the mean ionic activity coefficient, the OPAS (osmotic pressure for the activity of selvents) method was applied. The new models extend an ion model family for the alkali halides developed in previous works of our group in a consistent way.

Combining Machine Learning with Physical Knowledge in Thermodynamic Modeling of Fluid Mixtures.

Thermophysical properties of fluid mixtures are important in many fields of science and engineering. However, experimental data are scarce in this field, so prediction methods are vital. Different types of physical prediction methods are available, ranging from molecular models over equations of state to models of excess properties. These well-established methods are currently being complemented by new methods from the field of machine learning (ML). This review focuses on the rapidly developing interface between these two approaches and gives a structured overview of how physical modeling and ML can be combined to yield hybrid models. We illustrate the different options with examples from recent research and give an outlook on future developments.

Rational method for defining and quantifying pseudo-components based on NMR spectroscopy.

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 quantifying pseudo-components in such mixtures that is free of ad hoc assumptions. The new method requires only standard nuclear magnetic resonance (NMR) experiments and can be fully automated. In the first step, the method analyzes the composition of the poorly specified mixture in terms of structural groups, which is much easier than obtaining the component speciation. The structural groups are then clustered into pseudo-components based on information on the self-diffusion coefficients measured by pulsed-field gradient (PFG) NMR spectroscopy. We demonstrate the performance of the new method on several aqueous mixtures. The method is broadly applicable and provides a sound basis for modeling and simulation of processes with poorly specified mixtures, without the need for tedious and expensive structure elucidation. It is also attractive for process monitoring.

Mass Transfer through Vapor-Liquid Interfaces Studied by Non-Stationary Molecular Dynamics Simulations.

Molecular dynamics (MD) simulations are highly attractive for studying the influence of interfacial effects, such as the enrichment of components, on the mass transfer through the interface. In a recent work, we have presented a steady-state MD simulation method for investigating this phenomenon and tested it using model mixtures with and without interfacial enrichment. The present study extends this work by introducing a non-stationary MD simulation method. A rectangular simulation box that contains a mixture of two components 1 + 2 with a vapor phase in the middle and two liquid phases on both sides is used. Starting from a vapor-liquid equilibrium state, a non-stationary molar flux of component 2 is induced by inserting particles of component 2 into the center of the vapor phase in a pulse-like manner. During the isothermal relaxation process, particles of component 2 pass through the vapor phase, cross the vapor-liquid interface, and enter the liquid phase. The system thereby relaxes into a new vapor-liquid equilibrium state. During the relaxation process, spatially resolved responses for the component densities, fluxes, and pressure are sampled. To reduce the noise and provide measures for the uncertainty of the observables, a set of replicas of simulations is carried out. The new simulation method was applied to study mass transfer in two binary Lennard-Jones mixtures: one that exhibits a strong enrichment of the low-boiling component 2 at the vapor-liquid interface and one that shows no enrichment. Even though both mixtures have similar transport coefficients in the bulk phases, the results for mass transfer differ significantly, indicating that the interfacial enrichment influences the mass transfer.

Comparison of Force Fields for the Prediction of Thermophysical Properties of Long Linear and Branched Alkanes.

The prediction of thermophysical properties at extreme conditions is an important application of molecular simulations. The quality of these predictions primarily depends on the quality of the employed force field. In this work, a systematic comparison of classical transferable force fields for the prediction of different thermophysical properties of alkanes at extreme conditions, as they are encountered in tribological applications, was carried out using molecular dynamics simulations. Nine transferable force fields from three different classes were considered (all-atom, united-atom, and coarse-grained force fields). Three linear alkanes (n-decane, n-icosane, and n-triacontane) and two branched alkanes (1-decene trimer and squalane) were studied. Simulations were carried out in a pressure range between 0.1 and 400 MPa at 373.15 K. For each state point, density, viscosity, and self-diffusion coefficient were sampled, and the results were compared to experimental data. The Potoff force field yielded the best results.

Prediction of parameters of group contribution models of mixtures by matrix completion.

Group contribution (GC) methods are widely used for predicting the thermodynamic properties of mixtures by dividing components into structural groups. These structural groups can be combined freely so that the applicability of a GC method is only limited by the availability of its parameters for the groups of interest. For describing mixtures, pairwise interaction parameters between the groups are of prime importance. Finding suitable numbers for these parameters is often impeded by a lack of suitable experimental data. Here, we address this problem by using matrix completion methods (MCMs) from machine learning to predict missing group-interaction parameters. This new approach is applied to UNIFAC, an established group contribution method for predicting activity coefficients in mixtures. The developed MCM yields a complete set of parameters for the first 50 main groups of UNIFAC, which substantially extends the scope and applicability of UNIFAC. The quality of the predicted parameter set is evaluated using vapor-liquid equilibrium data of binary mixtures from the Dortmund Data Bank. This evaluation reveals that our approach gives prediction accuracies comparable with UNIFAC for data sets to which UNIFAC was fitted, and only slightly lower accuracies for data sets to which UNIFAC is not applicable.

Adsorption of conjugates of lysozyme and fluorescein isothiocyanate in hydrophobic interaction chromatography.

Bioconjugates, such as antibody-drug conjugates or fluorescent-labeled proteins, are highly interesting for various applications in medicine and biology. In their production, not only the synthesis is challenging but also the downstream processing, for which hydrophobic interaction chromatography (HIC) is often used. However, in-depth studies of the adsorption of bioconjugates in HIC are still rare. Therefore, in the present work, three different conjugates of lysozyme and fluorescein isothiocyanate (FITC) were synthesized and isolated, and their adsorption on the hydrophobic resin Toyopearl PPG-600 M was systematically studied in batch experiments. The influence of sodium chloride and ammonium sulfate with ionic strengths up to 2000 mM on the adsorption isotherms was investigated at pH 7.0 and 25 °C, and the results were compared to those for pure lysozyme. The conjugation leads to an increase of the adsorption in all studied cases. All studied conjugates contain only a single FITC and differ only in the position of the conjugation on the lysozyme. Despite this, strong differences in the adsorption behavior were observed. Moreover, a mathematical model was developed, which enables the prediction of the adsorption isotherms in the studied systems for varying ionic strengths.

Functionalized Controlled Porous Glasses for Producing Radical-Free Hyperpolarized Liquids by Overhauser DNP.

Overhauser dynamic nuclear polarization (ODNP) can be used as a tool for NMR signal enhancement and happens on very short time scales. Therefore, ODNP is well suited for the measurement of fast-flowing samples, even in compact magnets, which is beneficial for the real-time monitoring of chemical reactions or processes. ODNP requires the presence of unpaired electrons in the sample, which is usually accomplished by the addition of stable radicals. However, radicals affect the nuclear relaxation times and can hamper the NMR detection. This is circumvented by immobilizing radicals in a packed bed allowing for the measurement of radical-free samples when using ex situ DNP techniques (DNP build-up and NMR detection happen at different places) and flow-induced separation of the hyperpolarized liquid from the radicals. Therefore, the synthesis of robust and chemically inert immobilized radical matrices is mandatory. In the present work, this is accomplished by immobilizing the radical glycidyloxy-tetramethylpiperidinyloxyl with a polyethyleneimine (PEI) linker on the surface of controlled porous glasses (CPG). Both the porosity of the CPGs and also the size of the PEI-linker were varied, resulting in a set of distinct radical matrices for continuous-flow ODNP. The study shows that CPGs with PEI-linkers provide robust, inert and efficient ODNP matrices.

Accurate measurements of self-diffusion coefficients with benchtop NMR using a QM model-based approach.

The measurement of self-diffusion coefficients using pulsed-field gradient (PFG) nuclear magnetic resonance (NMR) spectroscopy is a well-established method. Recently, benchtop NMR spectrometers with gradient coils have also been used, which greatly simplify these measurements. However, a disadvantage of benchtop NMR spectrometers is the lower resolution of the acquired NMR signals compared to high-field NMR spectrometers, which requires sophisticated analysis methods. In this work, we use a recently developed quantum mechanical (QM) model-based approach for the estimation of self-diffusion coefficients from complex benchtop NMR data. With the knowledge of the species present in the mixture, signatures for each species are created and adjusted to the measured NMR signal. With this model-based approach, the self-diffusion coefficients of all species in the mixtures were estimated with a discrepancy of less than 2 % compared to self-diffusion coefficients estimated from high-field NMR data sets of the same mixtures. These results suggest benchtop NMR is a reliable tool for quantitative analysis of self-diffusion coefficients, even in complex mixtures.

Making thermodynamic models of mixtures predictive by machine learning: matrix completion of pair interactions.

Predictive models of thermodynamic properties of mixtures are paramount in chemical engineering and chemistry. Classical thermodynamic models are successful in generalizing over (continuous) conditions like temperature and concentration. On the other hand, matrix completion methods (MCMs) from machine learning successfully generalize over (discrete) binary systems; these MCMs can make predictions without any data for a given binary system by implicitly learning commonalities across systems. In the present work, we combine the strengths from both worlds in a hybrid approach. The underlying idea is to predict the pair-interaction energies, as they are used in basically all physical models of liquid mixtures, by an MCM. As an example, we embed an MCM into UNIQUAC, a widely-used physical model for the Gibbs excess energy. We train the resulting hybrid model in a Bayesian machine-learning framework on experimental data for activity coefficients in binary systems of 1146 components from the Dortmund Data Bank. We thereby obtain, for the first time, a complete set of UNIQUAC parameters for all binary systems of these components, which allows us to predict, in principle, activity coefficients at arbitrary temperature and composition for any combination of these components, not only for binary but also for multicomponent systems. The hybrid model even outperforms the best available physical model for predicting activity coefficients, the modified UNIFAC (Dortmund) model.

Investigation of Radial Shaft Seal Swelling Using a Special Tribometer and Magnetic Resonance Imaging.

Compatibility between the rubber material of radial shaft seals and the lubricants to be sealed is an important requirement that customers demand of their lubricant suppliers. Among other effects that may result from incompatibility, the penetration of lubricant components into the rubber (swelling) can impair the seal's functionality due to changes in its geometry and mechanical behavior. Typically, the penetration of a lubricant into an elastomer is evaluated after an immersion test using volumetric, gravimetric, and extraction measurements. Due to the small changes that need to be detected, such methods may not be sufficient to obtain meaningful results. In this contribution, we use magnetic resonance imaging (MRI) to investigate swelling on special tribometer samples as well as a radial shaft seal that were previously used in component tests. Several combinations of rubbers and lubricants that have proven to be compatible were tested in addition to combinations with expected incompatibilities in real applications. The results indicate that MRI measurements can be used to quantify the penetration depth and potentially also the velocity with which the lubricant diffuses into the rubber, thereby yielding detailed insights into the swelling process of the seal.

Attribute-based Explanation of Non-Linear Embeddings of High-Dimensional Data.

Embeddings of high-dimensional data are widely used to explore data, to verify analysis results, and to communicate information. Their explanation, in particular with respect to the input attributes, is often difficult. With linear projects like PCA the axes can still be annotated meaningfully. With non-linear projections this is no longer possible and alternative strategies such as attribute-based color coding are required. In this paper, we review existing augmentation techniques and discuss their limitations. We present the Non-Linear Embeddings Surveyor (NoLiES) that combines a novel augmentation strategy for projected data (rangesets) with interactive analysis in a small multiples setting. Rangesets use a set-based visualization approach for binned attribute values that enable the user to quickly observe structure and detect outliers. We detail the link between algebraic topology and rangesets and demonstrate the utility of NoLiES in case studies with various challenges (complex attribute value distribution, many attributes, many data points) and a real-world application to understand latent features of matrix completion in thermodynamics.

Influence of pH value and salts on the adsorption of lysozyme in mixed-mode chromatography.

Mixed-mode chromatography (MMC) is an interesting technique for challenging protein separation processes which typically combines adsorption mechanisms of ion exchange (IEC) and hydrophobic interaction chromatography (HIC). Adsorption equilibria in MMC depend on multiple parameters but systematic studies on their influence are scarce. In the present work, the influence of the pH value and ionic strengths up to 3000 mM of four technically relevant salts (sodium chloride, sodium sulfate, ammonium chloride, and ammonium sulfate) on the lysozyme adsorption on the mixed-mode resin Toyopearl MX-Trp-650M was studied systematically at 25℃. Equilibrium adsorption isotherms at pH 5.0 and 6.0 were measured and compared to experimental data at pH 7.0 from previous work. For all pH values, an exponential decay of the lysozyme loading with increasing ionic strength was observed. The influence of the pH value was found to depend significantly on the ionic strength with the strongest influence at low ionic strengths where increasing pH values lead to decreasing lysozyme loadings. Furthermore, a mathematical model that describes the influence of salts and the pH value on the adsorption of lysozyme in MMC is presented. The model enables predicting adsorption isotherms of lysozyme on Toyopearl MX-Trp-650M for a broad range of technically relevant conditions.