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.

Hierarchical Multicriteria Optimization of Molecular Models of Water.

Many widely used molecular models of water are built from a single Lennard-Jones site on which three point charges are positioned, one negative and two positive ones. Models from that class, denoted LJ3PC here, are computationally efficient, but it is well known that they cannot represent all relevant properties of water simultaneously with good accuracy. Despite the importance of the LJ3PC water model class, its inherent limitations in simultaneously describing different properties of water have never been studied systematically. This task can only be solved by multicriteria optimization (MCO). However, due to its computational cost, applying MCO to molecular models is a formidable task. We have recently introduced the reduced units method (RUM) to cope with this problem. In the present work, we apply the RUM in a hierarchical scheme to optimize LJ3PC water models taking into account five objectives: the representation of vapor pressure, saturated liquid density, self-diffusion coefficient, shear viscosity, and relative permittivity. Of the six parameters of the LJ3PC models, five were varied; only the H-O-H bond angle, which is usually chosen based on physical arguments, was kept constant. Our hierarchical RUM-based approach yields a Pareto set that contains attractive new water models. Furthermore, the results give an idea of what can be achieved by molecular modeling of water with models from the LJ3PC class.

Prediction of pair interactions in mixtures by matrix completion.

Molecular simulations enable the prediction of physicochemical properties of mixtures based on pair-interaction models of the pure components and combining rules to describe the unlike interactions. However, if no adjustment to experimental data is made, the existing combining rules often do not yield sufficiently accurate predictions of mixture data. To address this problem, adjustable binary parameters ξij describing the pair interactions in mixtures (i + j) are used. In this work, we present the first method for predicting ξij for unstudied mixtures based on a matrix completion method (MCM) from machine learning (ML). Considering molecular simulations of Henry's law constants as an example, we demonstrate that ξij for unstudied mixtures can be predicted with high accuracy. Using the predicted ξij significantly increases the accuracy of the Henry's law constant predictions compared to using the default ξij = 1. Our approach is generic and can be transferred to molecular simulations of other mixture properties and even to combining rules in equations of state, granting predictive access to the description of unlike intermolecular interactions.

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.

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.

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.

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.

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.

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.

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.

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.

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.

High Flow-Rate Benchtop NMR Spectroscopy Enabled by Continuous Overhauser DNP.

Analysis of a fast-flowing liquid with NMR spectroscopy is challenging because short residence times in the magnetic field of the spectrometer result in inefficient polarization buildup and thus poor signal intensity. This is particularly problematic for benchtop NMR spectrometers because of their compact design. Therefore, in the present work, different methods to counteract this prepolarization problem in benchtop NMR spectroscopy were studied experimentally. The tests were carried out with an equimolar acetonitrile + water mixture flowing through a capillary with a 0.25 mm inner diameter at flow rates up to 2.00 mL min-1, corresponding to mean velocities of up to 0.7 m s-1. Established approaches gave only poor results at high flow rates, namely, using a prepolarization magnet, using a loopy flow cell, and using a T1 relaxation agent. To overcome this, signal enhancement by Overhauser dynamic nuclear polarization (ODNP) was used, which is based on polarization transfer from unpaired electron spins to nuclear spins and happens on very short time scales, resulting in high signal enhancements, also in fast-flowing liquids. A corresponding setup was developed and used for the studies: the line leading to the 1 T benchtop NMR spectrometer first passes through a fixed bed with a radical matrix placed in a Halbach magnet equipped with a microwave cavity to facilitate the spin transfer. With this ODNP setup, excellent results were obtained even for the highest studied flow rates. This shows that ODNP is an enabler for fast-flow benchtop NMR spectroscopy.

Molecular Dynamics Study of Wetting and Adsorption of Binary Mixtures of the Lennard-Jones Truncated and Shifted Fluid on a Planar Wall.

The wetting of surfaces is strongly influenced by adsorbate layers. Therefore, in this work, sessile drops and their interaction with adsorbate layers on surfaces were investigated by molecular dynamics simulations. Binary fluid model mixtures were considered. The two components of the fluid mixture have the same pure component parameters, but one component has a stronger and the other a weaker affinity to the surface. Furthermore, the unlike interactions between both components were varied. All interactions were described by the Lennard-Jones truncated and shifted potential with a cutoff radius of 2.5σ. The simulations were carried out at constant temperature for mixtures of different compositions. The parameters were varied systematically and chosen such that cases with partial wetting as well as cases with total wetting were obtained and the relation between the varied molecular parameters and the phenomenological behavior was elucidated. Data on the contact angle as well as on the mole fraction and thickness of the adsorbate layer were obtained, accompanied by information on liquid and gaseous bulk phases and the corresponding phase equilibrium. Also, the influence of the adsorbate layer on the wetting was studied: for a sufficiently thick adsorbate layer, the wall's influence on the wetting vanishes, which is then only determined by the adsorbate layer.

Automated Methods for Identification and Quantification of Structural Groups from Nuclear Magnetic Resonance Spectra Using Support Vector Classification.

Nuclear magnetic resonance (NMR) spectroscopy is a powerful tool for elucidating the structure of unknown components and the composition of liquid mixtures. However, these tasks are often tedious and challenging, especially if complex samples are considered. In this work, we introduce automated methods for the identification and quantification of structural groups in pure components and mixtures from NMR spectra using support vector classification. As input, a 1H NMR spectrum and a 13C NMR spectrum of the liquid sample (pure component or mixture) that is to be analyzed is needed. The first method, called group-identification method, yields qualitative information on the structural groups in the sample. The second method, called group-assignment method, provides the basis for a quantitative analysis of the sample by identifying the structural groups and assigning them to signals in the 13C NMR spectrum of the sample; quantitative information can then be obtained with readily available tools by simple integration. We demonstrate that both methods, after being trained to NMR spectra of nearly 1000 pure components, yield excellent predictions for pure components that were not part of the training set as well as mixtures. The structural group-specific information obtained with the presented methods can, e.g., be used in combination with thermodynamic group-contribution methods to predict fluid properties of unknown samples.

Interfacial properties of binary mixtures of simple fluids and their relation to the phase diagram.

Interfacial properties of binary fluid mixtures were studied using both molecular dynamics (MD) simulations and density gradient theory (DGT). The focus of the study is on the relation of the interfacial properties to the phase diagram of the mixture. Two binary Lennard-Jones mixtures were investigated in a wide range of states: a highly asymmetric mixture (type III), which exhibits vapour-liquid equilibria (VL1E and VL2E), liquid-liquid equilibria (L1L2E), a three-phase equilibrium (VL1L2E), and supercritical fluid-fluid equilibria (F1F2E), and, as a reference, an ideal mixture (type I). The studied interfacial properties are: the surface tension, the relative adsorption, the width of the interfacial region, and the enrichment of the low-boiling component, on which we set a focus. Enrichment was observed at VL1 interfaces; and, to a small extent, also at L1L2 interfaces; but not at the supercritical F1F2 interfaces. The large enrichment found at VL1 interfaces of the type III mixture can be interpreted as a wetting transition: approaching the VL1L2E three-phase line from the VL1 side, the enrichment gets stronger and can be interpreted as precursor of the second liquid phase L2. However, the actual existence of a three-phase line in the phase diagram is no prerequisite for an enrichment. The enrichment is found to be highly temperature-dependent and increases with decreasing temperature.

The Influence of Lubrication and the Solid-Fluid Interaction on Thermodynamic Properties in a Nanoscopic Scratching Process.

Liquid lubricants play an important role in contact processes; for example, they reduce friction and cool the contact zone. To gain better understanding of the influence of lubrication on the nanoscale, both dry and lubricated scratching processes in a model system are compared in the present work using molecular dynamics simulations. The entire range between total dewetting and total wetting is investigated by tuning the solid-fluid interaction energy. The investigated scratching process consists of three sequential movements: A cylindrical indenter penetrates an initially flat substrate, then scratches in the lateral direction, and is finally retracted out of the contact with the substrate. The indenter is fully submersed in the fluid in the lubricated cases. The substrate, the indenter, and the fluid are described by suitably parametrized Lennard-Jones model potentials. The presence of the lubricant is found to have a significant influence on the friction and on the energy balance of the process. The thermodynamic properties of the lubricant are evaluated in detail. A correlation of the simulation results for the profiles of the temperature, density, and pressure of the fluid in the vicinity of the chip is developed. The work done by the indenter is found to mainly dissipate and thereby heat up the substrate and eventually the fluid. Only a minor part of the work causes plastic deformation of the substrate.

Thermophysical Properties of the Lennard-Jones Fluid: Database and Data Assessment.

Literature data on the thermophysical properties of the Lennard-Jones fluid, which were sampled with molecular dynamics and Monte Carlo simulations, were reviewed and assessed. The literature data were complemented by simulation data from the present work that were taken in regions in which previously only sparse data were available. Data on homogeneous state points (for given temperature T and density ρ: pressure p, thermal expansion coefficient α, isothermal compressibility ß, thermal pressure coefficient γ, internal energy u, isochoric heat capacity cv, isobaric heat capacity cp, Grüneisen parameter Γ, Joule-Thomson coefficient µJT, speed of sound w, Helmholtz energy a, and chemical potential) were considered, as well as data on the vapor-liquid equilibrium (for given T: vapor pressure ps, saturated liquid and vapor densities ρ' and ρ″, respectively, enthalpy of vaporization Δhv, and as well as surface tension γ). The entire set of available data, which contains about 35 000 data points, was digitalized and included in a database, which is made available in the Supporting Information of this paper. Different consistency tests were applied to assess the accuracy and precision of the data. The data on homogeneous states were evaluated pointwise using data from their respective vicinity and equations of state. Approximately 10% of all homogeneous bulk data were discarded as outliers. The vapor-liquid equilibrium data were assessed by tests based on the compressibility factor, the Clausius-Clapeyron equation, and by an outlier test. Seven particularly reliable vapor-liquid equilibrium data sets were identified. The mutual agreement of these data sets is approximately ±1% for the vapor pressure, ±0.2% for the saturated liquid density, ±1% for the saturated vapor density, and ±0.75% for the enthalpy of vaporization-excluding the region close to the critical point.