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The kinetics of chemical reactions in the liquid phase are often strongly determined by the reaction solvent. Consequently, the choice of the optimal solvent is an important task in chemical proces...

This work focuses on the ionic liquid (IL) synthesis starting from dimethyl carbonate and 1-ethylimidazole in methanol. In-situ IR-spectroscopy is used to gain time-dependent concentrations for kinetic analysis. The reaction is found to be self-accelerating, since the solvent polarity increases with the formation of the ionic product. This corresponds to a salting-out effect. Since the reaction rate is increasing in the beginning, pre-dosing of the product is proposed to enhance the overall productivity. Therefore, the space-time-yield is calculated as the arithmetic mean of the reaction rate with a varying starting point. It turns out that pre-dosing does not enhance the productivity here, since the space-time-yield of the complete reaction progress is below the initial reaction rate. Beyond that, two sharp bends can be observed in the progress of the reaction rate coefficient that point towards sudden changes in the reaction environment. The first bend can be assigned to a phase separation of the ionic liquid on a microscopical scale. The second bend takes place when the IL concentration surpasses roughly one sixth of the methanol concentration. Above that threshold, the whole amount of methanol is captured in the solvation shell of the anion monomethyl carbonate, as quantum chemical calculations suggest. Finally, the reaction rate coefficients were calculated using quantum chemical methods with respect to the solvent environment. The theoretical results show a good accordance with experimental values for the starting compositions. Thus, theoretical calculations help to estimate productive reaction mixtures.

Ultra-low cross-linked (ULC) microgels are extremely soft polymers with advanced material properties exhibiting a colloid-to-polymer transition. Poly(N-isopropylacrylamide) (pNIPAM) microgels are commonly synthesized by radical precipitation polymerization of the fully protonated monomer N-isopropylacrylamide (NIPAM). Analogous deuterated monomers arouse interest regarding their varying scattering length densities in small-angle neutron scattering (SANS). An isotope substitution in the main monomer of a microgel changes its scattering length density, which enables the exploitation of the technique of contrast variation in SANS. Here, we demonstrate that the synthesis of the deuterated pNIPAM ULC microgels encounters challenges related to the self-cross-linking mechanism of the polymer chains. The location of the deuterium isotopes is crucial for the generation of deuterated pNIPAM ULC microgels: when the isopropyl group of NIPAM is deuterated, the cross-linking is strongly restrained and the formation of microgels is precluded. However, the deuteration of the vinyl group of NIPAM, ending as backbone in the pNIPAM chains, allows the self-cross-linking. Ab initio calculations of the bond dissociation enthalpies endorse that the cross-linking of the pNIPAM chains occurs via hydrogen atom abstraction at the tertiary carbon atom of the isopropyl group. Additionally, we show that the deuteration of the vinyl group barely shifts the transition temperature of the polymer compared to protonated pNIPAM, whereas deuteration of the isopropyl group results in a significant shift of the transition temperature toward higher temperatures. Finally, the deuterated ULC micro- and nanogels reveal a stronger cross-linked network with lower swelling ability compared to the protonated microgels.

Molecular systems design & engineering 3(4), 645-657 (2018). doi:10.1039/C7ME00125H, Published by Royal Society of Chemistry, Cambridge

A new quantitative structure–property relationships (QSPR) based prediction method for fuel lubricity is proposed to enable fast screening of potential biofuels. The lubricity of 75 substances is experimentally determined with a High-Frequency Reciprocating Rig (HFRR). The list of substances includes potential biofuels and fluids with similar molecular structures. Molecular descriptors for the model are selected by the elastic net regression method. The model predictions enable the discrimination of good and insufficient lubricants.

Computer-Aided Molecular Design (CAMD) enables the automated exploration of chemical space and thus offers great possibilities for efficient design of chemical products. The key to reliable CAMD is a sound prediction of the properties of desired products, where quantum chemistry-based (quantum chemical, QC) prediction methods offer unique opportunities. In this article, we discuss CAMD methods based on QC and highlight two important fields of application: the design of solvents and of molecular catalysts. Screening of separation solvents based on physical property targets can be regarded as established by now. However, the integration of molecular design and process design remains an important challenge. For the design of reactive systems, transition state theory provides a sound basis. However, efficient CAMD methods and tools based on quantum chemistry are still in their infancy. Recent results and the unexplored opportunities of quantum chemistry make the development of QC-based CAMD methods a promising field of research., Current Opinion in Chemical Engineering, 27, ISSN:2211-3398

Utilizing CO2 to produce value-added chemicals can save environmental and economic impacts. However, these savings are reduced by the cost of CO2 supply when CO2 has to be captured from dilute sour...

Parameterizing equations of state is often a compromise between minimizing experimental effort and maximizing accuracy of the calculated thermodynamic properties. Parameter prediction methods can avoid experimental effort altogether. Alternatively, we need at least as many experimental property data points as the number of model parameters to parameterize the equation of state. Thus, for PCP-SAFT, we need at least three experimental data points corresponding to the three parameters required to model non-associating components. In this work, we present a systematic approach to combine predicted parameters with data points chosen by Optimal Experimental Design. This approach allows to find a trade-off between experimental effort and accuracy of the estimated thermodynamic properties. Predicted parameters yield an average RMSD (root mean square relative deviation) of 40% from vapor pressure data. Combining predicted parameters with one optimally chosen data point for vapor pressure yields already an accuracy of 4% average RMSD in vapor pressure. The presented approach allows to obtain PCP-SAFT parameters which are more accurate than the predicted parameters at a lower experimental effort than required for parameter fitting without predictive information. Our results can serve as a guide for the practitioner to evaluate the minimal experimental effort necessary in order to reach a desired accuracy.

© 2016 American Chemical Society. NIPAM (N-isopropylacrylamide)-based polymers in water show many interesting properties in experiments, including a lower critical solution temperature (LCST) at 305 K and a conformational transition of single chains at the same temperature. The results of many simulation studies suggest that standard force fields are able to describe the conformational transition and the phase equilibrium well. We show by performing long molecular dynamics simulations of the direct liquid-liquid phase equilibrium of NIPAM trimers in water that there is no LCST in the expected temperature range for any of the force fields under study. The results show further that the relaxation times of single-chain simulations are considerably longer than anticipated. Conformational transitions of single polymers can therefore not necessarily be used as surrogates for a real phase transition.

The journal of chemical physics 148(1), 1-13 (2018). doi:10.1063/1.5009011, Published by American Institute of Physics, Melville, NY

Microgels based on poly(N-isopropylacrylamide)- and poly(N-vinylcaprolactam) can be synthesized by precipitation polymerization. To get a better insight into the kinetics of the microgel synthesis, a model-based approach is pursued. Herein, the approach proposed by Janssen et al. (2017) is extended to the terpolymerization system. The kinetic model for a two-phase terpolymerization is combined with parameter values from quantum mechanical calculations. The remaining unknown parameter values are estimated from experimental data using reaction calorimetry and Raman spectroscopy. Acceptable agreement of simulation and experimental measurements is obtained. The prediction of the gel phase growth is combined with the consumption of comonomers and the cross-linker N,N’-methylenebis(acrylamide) to predict the average distribution of comonomers, thus giving insight into the microgel structure. The resulting particle structures indicate uniform N-vinylcaprolactam and N-isopropylacrylamide compositions in the core and an outer layer with a high N-vinylcaprolactam fraction, while the cross-linker distribution decreases from core to shell.

© 2017 American Chemical Society. The polymer poly(N-isopropylacrylamide) (PNIPAM) is studied using a novel combination of multiscale modeling methodologies. We develop an iterative Boltzmann inversion potential of concentrated PNIPAM solutions and combine it with lattice Boltzmann as a Navier-Stokes equation solver for the solvent. We study in detail the influence of the methodology on statics and dynamics of the system. The combination is successful and significantly simpler and faster than other mapping techniques for polymer solution while keeping the correct hydrodynamics. The model can semiquantitatively describe the correct phase behavior and polymer dynamics.

Thermodynamic properties are often modeled by classical force fields which describe the interactions on the atomistic scale. Molecular simulations are used for retrieving thermodynamic data from such models, and many simulation techniques and computer codes are available for that purpose. In the present round robin study, the following fundamental question is addressed: Will different user groups working with different simulation codes obtain coinciding results within the statistical uncertainty of their data? A set of 24 simple simulation tasks is defined and solved by five user groups working with eight molecular simulation codes: DL_POLY, GROMACS, IMC, LAMMPS, ms2, NAMD, Tinker, and TOWHEE. Each task consists of the definition of (1) a pure fluid that is described by a force field and (2) the conditions under which that property is to be determined. The fluids are four simple alkanes: ethane, propane, n-butane, and iso-butane. All force fields consider internal degrees of freedom: OPLS, TraPPE, and a modified OPLS version with bond stretching vibrations. Density and potential energy are determined as a function of temperature and pressure on a grid which is specified such that all states are liquid. The user groups worked independently and reported their results to a central instance. The full set of results was disclosed to all user groups only at the end of the study. During the study, the central instance gave only qualitative feedback. The results reveal the challenges of carrying out molecular simulations. Several iterations were needed to eliminate gross errors. For most simulation tasks, the remaining deviations between the results of the different groups are acceptable from a practical standpoint, but they are often outside of the statistical errors of the individual simulation data. However, there are also cases where the deviations are unacceptable. This study highlights similarities between computer experiments and laboratory experiments, which are both subject not only to statistical error but also to systematic error.

The journal of physical chemistry / B 121(13), 2887-2895 (2017). doi:10.1021/acs.jpcb.6b09147, Published by American Chemical Society, Washington, DC

Increasing demand for tailor-made chemicals gives rise to challenging molecular design tasks. Previous molecular design approaches have relied on simplified thermodynamic models to be computationally tractable. In contrast, quantum mechanics offers the most comprehensive molecular picture but a direct integration into computer-aided molecular design (CAMD) is challenging. In this work, we therefore aim at integrating quantum-level information into molecular design while still allowing for efficient computations. For this purpose, a framework for optimization-based molecular design is introduced based on property predictions by COSMO-RS and a genetic algorithm for molecular design. The resulting framework is applied to a case study for solvent design in liquid-liquid extraction.