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Many applications of responsive microgels rely on the fast adaptation of the polymer network. However, the underlying dynamics of the de-/swelling process of the gels have not been fully understood. In the present work, we focus on the collapse kinetics of poly-N-isopropylacrylamide (pNIPAM) microgels due to cononsolvency. Cononsolvency means that either of the pure solvents, e.g., pure water or pure methanol, act as a so-called good solvent, leading to a swollen state of the polymer network. However, in mixtures of water and methanol, the previously swollen network undergoes a drastic volume loss. To further elucidate the cononsolvency transition, pNIPAM microgels with diameters between 20 and 110 μm were synthesized by microfluidics. To follow the dynamics, pure water was suddenly exchanged with an unfavorable mixture of 20 mol% methanol (solvent-jump) within a microfluidic channel. The dynamic response of the microgels was investigated by optical and fluorescence microscopy and Raman microspectroscopy. The experimental data provide unique and detailed insight into the size-dependent kinetics of the volume phase transition due to cononsolvency. The change in the microgel's diameter over time points to a two-step process of the microgel collapse with a biexponential behavior. Furthermore, the dependence between the two time constants from this biexponential behavior and the microgel's diameter in the collapsed state deviates from the square-power law proposed by Tanaka and Fillmore [ J. Chem. Phys. 1979, 70, 1214-1218]. The deviation is discussed considering the adhesion-induced deformation of the gels and the physical processes underlying the collapse.

Synthetic dimethoxymethane (DMM) is a promising fuel or blend component as it offers outstanding combustion characteristics. DMM production from hydrogen (H2) and carbon dioxide (CO2) is technically feasible with established technology but results in a low overall process efficiency. Recent research in catalyst development has increased DMM yield significantly and new reaction pathways have been proposed. Yet, it remains unknown how the achievements in catalyst development affect process performance. To close this gap, we analyze processes based on five reaction pathways regarding exergy efficiency, production cost, and climate impact. As the pathways have different technology readiness levels, we develop a methodology that ensures consistent boundary conditions and model detail between pathways. The methodology enables a hierarchical optimization-based process design and evaluation. The results show that the non-oxidative (i.e., reductive, dehydrogenative, and transfer-hydrogenative) pathways consume stoichiometrically less H2 not only than the established and oxidative pathway, but also less than most other electricity-based fuels (e-fuels). The higher resource efficiency of these pathways increases process exergy efficiency from 75% to 84%; production cost (2.1$ Ldiesel-eq.−1) becomes competitive to other e-fuels; and the impact on climate change reduces by up to 92% compared to fossil diesel, if renewable electricity is utilized. Whereas the reductive pathway may already enable a sustainable production of DMM with only little catalyst improvements, the dehydrogenative and transfer-hydrogenative pathways still require a higher DMM selectivity and methanol conversion, respectively. With considerable catalyst improvements, a maximum exergy efficiency of 92% and minimum production cost of 2.0$ Ldiesel-eq.−1 are achievable. Our analyses show: With the non-oxidative pathways, the high potential of DMM is no longer restricted to its outstanding combustion characteristics but extended to its production., Energy & Environmental Science, 14 (7), ISSN:1754-5692, ISSN:1754-5706

Industrial applications such as batteries and bio-separations require modeling the thermodynamic properties of mixed solvent electrolytes. Thermodynamic models for electrolytes often consider the solvents as a dielectric continuum characterized by their dielectric constant. Therefore, accurate predictions require a physically sound model for the dielectric constant of mixed solvents, depending on temperature, pressure, and mixture composition. We present a physical model for the dielectric constant of pure solvents and mixtures based on perturbation theory. The analytical expression is third order in the dipole density. For each pure component, the model requires the dipole moment and two adjustable pure-component parameters. We apply the model to the binary mixtures methanol–water and ethylene glycol–water considering pure component experimental data for temperatures between 273.15 K to 823.15 K and pressures between 0.1 MPa and 1189.0 MPa. The presented model improves the prediction of the mixed solvent dielectric constant for both mixtures compared to the linear molar mixing rule, and achieves similar accuracies as the linear volume-based and mass-based mixing rules. We show that the model is suitable in the case of scarce experimental data., Fluid Phase Equilibria, 555, ISSN:0378-3812

The combination of microfluidics and Raman microspectroscopy has proven to reduce the time and amount of materials required to determine liquid–liquid equilibrium (LLE) data. Until now, the experiments have been conducted manually. However, many applications have shown that the highest efficiency and user independence can be reached by automation. Therefore, we developed an automated setup and workflow from calibration to data analysis for the determination of liquid–liquid equilibrium data using Raman microspectroscopy and a microfluidic platform. Pure components are premixed online using a micromixer, resulting in a closed system with the additional advantage of avoiding potential losses of volatile components. In the automated setup, one experiment generates several data points for calibration and LLE data measurements. The automated setup and workflow are successfully validated with respect to both the integrated calibration and the LLE measurements. For this purpose, we studied two ternary systems (c...

Due to the threat of climate change, renewable feedstocks & alternative energy carriers are becoming more necessary than ever. One key vector is hydrogen, which can fulfil these roles and is a renewable resource when split from water using renewable electricity. Electrolyzers are often not designed for variable operation, such as power from sources like wind or solar. This work develops a framework to optimize the design and operation of a large-scale electrolyzer hub under variable power supply. The framework is a two-part optimization, where designs of repeated, modular units are optimized, then the entire system is optimized based on those modular units. The framework is tested using a case study of an electrolyzer hub powered by a Dutch wind farm to minimize the levelized cost of hydrogen. To understand how the optimal design changes, three power profiles are examined, including a steady power supply, a representative wind farm power supply, and the same wind farm power supply compressed in time. The work finds the compressed power profile uses PEM technology which can ramp up and down more quickly. The framework determines for this case study, pressurized alkaline electrolyzers with large stacks are the cheapest modular unit, and while a steady power profile resulted in the cheapest hydrogen, costing 4.73 €/kg, the typical wind power profile only raised the levelized cost by 2%–4.82 €/kg. This framework is useful for designing large-scale electrolysis plants and understanding the impact of specific design choices on the performance of a plant.

Reducing net emission The great majority of plastics in current use are sourced from fossil fuels, with additional fossil fuels combusted to power their manufacture. Substantial research is focused on finding more sustainable building blocks for next-generation polymers. Meys et al . report a series of life cycle analyses suggesting that even the current varieties of commercial monomers could potentially be manufactured and polymerized with no net greenhouse gas emissions. The cycle relies on combining recycling of plastic waste with chemical reduction of carbon dioxide captured from incineration or derived from biomass. —JSY

Diffusion data in multicomponent liquids are scarce, because these diffusion measurements are time-consuming and laborious. Most diffusion data are therefore available for binary mixtures. While th...

Elastomers have been recently proposed to integrate CO2 as carbon feedstock. These elastomers are produced by reacting carbon dioxide with propylene oxide and maleic anhydride. The resulting cross-linkable polyether carbonate polyols can be combined with isocyanates to form a novel class of polymers: CO2-based rubbers. These CO2-based rubbers are able to substitute conventional rubbers in synthetic elastomer products, such as sealants or flexible tubes. In this work, we present the first Life Cycle Assessment for CO2-based rubbers. To compare CO2-based and conventional rubbers our assessment considers all relevant life cycle stages from cradle-to-grave. The production system of CO2-based rubbers encompasses a nearby ammonia plant as a CO2 source, the conversion of CO2 to polyols, the reaction of polyols with isocyanates and finally, the incineration of CO2-based rubbers. Our analysis shows that CO2-based rubbers containing approx. 20% wt. CO2 have a global warming impact of 4.93 kg CO2-eq. Thus, CO2-based rubbers are no carbon sink. However, CO2-based rubbers reduce global warming impact by up to 34% if they substitute, for example, hydrogenated nitrile butadiene rubber on an equal mass basis. Fossil resource depletion is reduced by up to 33%. In contrast, other impact categories like ionizing radiation are increased by the utilization of CO2-based rubbers in some cases. Thus, our study indicates that CO2-based rubbers provide a promising pathway to reduce global warming impact and fossil resource depletion. However, it is likely that some other environmental impact categories such as ionizing radiation and freshwater eutrophication are increased.

Bio-based chemicals are seen as a potential stepping-stone towards a more sustainable chemical industry. However, while bio-based routes are already available for various organic alcohols and acids, the direct bio-based production of aromatic compounds has been difficult so far. Recently, the first bio-based process for the production of the important aromatic aniline has been realized. The process produces bio-based aniline via two-stages: first, sugar is fermented by Corynebacterium Glutamicum to aminobenzoic acid, which is then catalytically decarboxylated to aniline. In this study, we present a prospective Life Cycle Assessment for bio-based aniline production to evaluate the environmental potential compared to fossil-based aniline production. Our results suggest that the bio-based production could reduce the global warming impacts of aniline from cradle-to-grave by 35–69% relative to fossil-based production, depending on the type of biomass feedstock. However, bio-based aniline could also substantially increase eutrophication and acidification, a trade-off commonly observed for bio-based processes. Thus, the novel production route is promising and expands the scope of bio-based chemicals towards aromatics., Journal of Cleaner Production, 290, ISSN:0959-6526

Current climate targets require negative carbon dioxide (CO2) emissions. Direct air capture is a promising negative emission technology, but energy and material demands lead to trade-offs with indirect emissions and other environmental impacts. Here, we show by life-cycle assessment that the commercial direct air capture plants in Hinwil and Hellisheiði operated by Climeworks can already achieve negative emissions today, with carbon capture efficiencies of 85.4% and 93.1%. The climate benefits of direct air capture, however, depend strongly on the energy source. When using low-carbon energy, as in Hellisheiði, adsorbent choice and plant construction become more important, inducing up to 45 and 15 gCO2e per kilogram CO2 captured, respectively. Large-scale deployment of direct air capture for 1% of the global annual CO2 emissions would not be limited by material and energy availability. However, the current small-scale production of amines for the adsorbent would need to be scaled up by more than an order of magnitude. Other environmental impacts would increase by less than 0.057% when using wind power and by up to 0.30% for the global electricity mix forecasted for 2050. Energy source and efficiency are essential for direct air capture to enable both negative emissions and low-carbon fuels. Direct air capture (DAC) of CO2 has garnered interest as a negative emissions technology to help achieve climate targets, but indirect emissions and other environmental impacts must be better understood. Here, Deutz and Bardow perform a life-cycle assessment of DAC plants operated by Climeworks, based on industrial data.

The chemistry of urethanes plays a key role in important industrial processes. Although catalysts are often used, the study of the reactions without added catalysts provides the basis for a deeper understanding. For the non‐catalytic urethane formation and cleavage reactions, the dominating reaction mechanism has long been debated. To our knowledge, the reaction kinetics have not been predicted quantitatively so far. Therefore, we report a new computational study of urethane formation and cleavage reactions. To analyze various potential reaction mechanisms and to predict the reaction rate constants quantum chemistry and transition state theory were employed. For validation, experimental data from literature and from own experiments were used. Quantitative agreement of experiments and predictions could be demonstrated. The calculations confirm earlier assumptions that urethane formation reactions proceed via mechanisms where alcohol molecules act as auto‐catalysts. Our results show that it is essential to consider several transition states corresponding to different reaction orders to enable agreement with experimental observations. Urethane cleavage seems to be catalyzed by an isourethane, leading to an observed 2nd‐order dependence of the reaction rate on the urethane concentration. The results of our study support a deeper understanding of the reactions as well as a better description of reaction kinetics and will therefore help in catalyst development and process optimization., Reaction kinetics and mechanisms of urethane formation and cleavage reactions were investigated based on advanced quantum chemical methods. These reactions represent important model reactions in urethane chemistry. The results confirm that both urethane formation and cleavage proceed via autocatalytic mechanisms. Several mechanisms contribute to the observed reaction rate for urethane formation. A validation using own experimental data and literature data confirmed that the used quantum chemical methods yield quantitative predictions.

Journal of cleaner production 322, 129144 (2021). doi:10.1016/j.jclepro.2021.129144, Published by Elsevier Science, Amsterdam [u.a.]

Green chemistry : GC 22(12), 3842-3859 (2020). doi:10.1039/C9GC04440J, Published by RSC, Cambridge

Capturing and utilizing CO2 as carbon feedstock for chemicals, fuels, or polymers is frequently discussed to replace fossil carbon and thereby help mitigate climate change. Emission reductions by Carbon Capture and Utilization (CCU) depend strongly on the choice of the CO2 source because CO2 sources differ in CO2 concentration and the resulting energy demand for capture. From a climate-change perspective, CO2 should be captured at the CO2 source with the lowest CO2 emissions from capture. However, reported carbon footprints differ widely for CO2 captured, from strongly negative to strongly positive for the same source. The differences are due to methodological ambiguity in the treatment of multifunctionality in current assessment practice. This paper reviews methodological approaches for determining the carbon footprint of captured CO2 as carbon feedstock, and shows why some approaches lead to suboptimal choices of CO2 sources and that increased consistency in life cycle assessment (LCA) studies on CCU is needed. Based on strict application of Life Cycle Assessment (LCA) standards and guidelines, it is shown that substitution should be applied to avoid suboptimal choices of CO2 sources. The resulting methodological recommendations are applied to estimate the carbon footprint of feedstock CO2 for current CO2 sources in Europe and for future CO2 sources in a scenario for a low carbon economy. For all CO2 sources, the cradle-to-gate footprint of captured CO2 is negative ranging from −0.95 to −0.59 kg CO2 eq. per kg of feedstock CO2 today and from −0.99 to −0.98 kg CO2 eq. in a low carbon economy. The carbon footprints of different CO2 sources differ mainly due to their energy demands. The presented assessment method and the carbon footprints of the CO2 feedstocks CO2 provide the basis for future assessments of carbon capture and utilization processes., Energy & Environmental Science, 13 (9), ISSN:1754-5692, ISSN:1754-5706

Vapor-liquid equilibrium data of fuel mixtures are of major importance for both fuel production as well as combustion. However, the measurement of vapor-liquid equilibrium data usually requires significant experimental effort. The experimental effort is particularly high if multicomponent mixtures are of interest, as experimental effort rises strongly with a rising number of components. In this work, we efficiently characterize the vapor-liquid equilibrium of a quaternary model biofuel and its binary subsystems. For this purpose, we employ the recently developed milliliter-scale Raman Spectroscopic Phase Equilibrium Characterization (RAMSPEQU)-setup. Vapor pressures are collected at T = 283–333 K, and isothermal pTx-data for mixtures at T = 303.2 K resulting in a pressure range of p = 2.8–81.9 kPa. The PCP-SAFT equation of state is used for thermodynamic modeling. Our binary data agrees well with experimental data from literature. The quaternary phase behavior is predicted with very good accuracy using PCP-SAFT with parameters adjusted to pure substance and binary mixture data only. The milliliter-scale setup allows us to characterize the phase equilibria with just 22 ml (binary) and less than 105 ml (quaternary) of the respective mixtures. The agreement of predicted and experimental quaternary phase equilibrium data indicate the reliability of the employed method for multicomponent vapor-liquid equilibrium measurements.

Hydrogen production from renewable electricity in power-to-gas concepts is promising for future energy storage systems since hydrogen offers high energy density and can be used emission free. Economically viable power-to-gas applications require high efficiency and thus low specific energy demand of the hydrogen production. Energy is required for hydrogen production via water electrolysis, but also for gas conditioning. Gas conditioning includes mechanical gas compression to a defined storage pressure and gas drying to purify the raw hydrogen. The energy demand of gas conditioning can be reduced by operating pressurized electrolyzers. However, pressurized operation increases the energy demand of the electrolyzer. To determine the optimal operating pressure of the electrolyzer, the overall power-to-gas process has to be considered. In this paper, the energy demand of the overall power-to-gas plants is optimized considering compression and temperature swing adsorption (TSA)-drying of hydrogen. It is shown that an optimum pressure for each operating condition in the electrolyzer in relation to the efficiency exists. This optimal operating pressure depends on the current density in the stack and the hydrogen storage pressure. When operating the system with load adapted operating pressure efficiencies between 55% and 73% for the whole power-to-gas plant can be achieved.

Catalytic methane decomposition (CMD) is promising for producing hydrogen without direct CO2 emissions. We estimate the CO2 mitigation costs associated with CMD for hydrogen production and subsequent power generation in a fuel cell. The overall CO2 emissions and economic viability are evaluated based on four scenarios: whether the by-product carbon can be sold or must be discarded into landfill; whether the catalyst can be recycled or not. CO2 emission savings and the associated costs of CMD concept are compared to the combined-cycle gas turbine (CCGT) power plant with and without carbon capture and storage (CCS). The results illustrate that the profitability of the concept as well as the ensuing CO2 abatement costs strongly depend on the ability to separate the catalysts from the carbon generated during the CMD. The life-cycle CO2 emissions per unit of electricity output of a CCGT plant with CCS are marginally higher than those generated in the CMD with perfect separation and regeneration of the catalysts. The levelized costs of electricity generation (LCOE) of CMD without selling the by-product are also higher than for CCGT with CCS. In contrast, the CMD can be highly profitable assuming selling the by-product carbon at current prices.

Spectral analysis of mixtures often faces challenges due to nonlinear effects such as peak shifts or strong background signals. Nonlinear mixture effects can be effectively treated by the Indirect Hard Modeling (IHM) Method. In IHM, mixture effects are captured by adapting hard models of pure component spectra when fitting a mixture model. However, IHM requires a suitable background treatment, which can become laborious. Background signals do not arise from the components of interest but often superimpose their spectra. In statistical methods for spectral analysis, background treatment is often conducted by derivatives of a spectrum. Derivatives effectively damp broad background signals. Standard IHM is not applicable to derivatives of spectra as the negative parts of a derivative spectrum cannot be modeled by pseudo-Voigt peaks which are always positive. In this work, we propose First-Derivative Indirect Hard Modeling (FD-IHM). FD-IHM uses the analytical derivatives of the peak functions. The analytical derivatives are fitted to numerical derivatives of the spectra. Thereby, we combine background treatment by first derivatives with the IHM method to treat nonlinear effects. The presented FD-IHM is validated using Raman spectra of ethanol/acetone mixtures. To introduce a variety of background signals, we used fluorescence dye, scattering bodies (yeast) and various background light sources. Classical IHM allows us to predict the test sets with a root-mean-square error of prediction (RMSEP) ranging from 0.60 wt% to 2.06 wt%, but careful manual background treatment had to be applied. With FD-IHM, we reduce the RMSEP error by 21%–73% without any background treatment. Thus, FD-IHM allows for both, efficient and accurate analysis of spectra with large background signals.

Greenhouse gas emissions from the transportation sector could be reduced by using biofuels. To avoid competition with the food chain, second generation biofuels produced from lignocellulosic biomass are of major interest. The interaction of second generation biofuels and water needs to be studied since water is known to have major impacts for first generation biofuels. In this work, we explore the water solubility and phase stability for second generation biofuels from catalytic conversion of biomass. Tetrahydrofurans are potential biofuels for compression ignition engines. We find that the water solubility in tetrahydrofurans is increased by a factor 200 compared to Diesel. In furans, suitable as fuels for spark ignition engines, the water solubility is 4 orders of magnitude larger than in gasoline. In blends of biofuels, water solubility can either be increased or decreased depending on the blend component. Water can strongly influence the miscibility of biofuel-blends: adding small amounts of water to a γ-valerolactone – di- n -butyl ether blend leads to phase separation resulting in two organic phases with different combustion behavior. At the same time, the biofuels studied dissolve much better in water, which is relevant for potential environmental impacts. The phase behavior with water is thus shown to be an important key performance indicator for the development of biofuels.

Experimental liquid–liquid equilibrium (LLE) data are indispensable for many applications ranging from extraction column design to water partitioning of organics in the environment. However, conventional LLE experiments are time-consuming and need large sample volumes. Therefore, a measurement setup is presented for the time and material efficient determination of LLE data. The measurement setup combines the advantages of microfluidics and Raman microspectroscopy: The small dimensions of the used H-cell microchannel lead to rapid equilibration and small sample consumption; Raman microspectroscopy allows for rapid in situ quantification of all components. The measurement setup has successfully been validated by measuring the LLE of the ternary system cyclohexane–methanol–toluene. Excellent agreement with the literature data has been achieved. Thus, the developed setup allows for the efficient determination of liquid–liquid equilibria in multicomponent mixtures.

Adsorptive drying systems aim at short drying times and small packaging. Thus, fast heat and mass transfer and high uptakes of the adsorbent are required. For this purpose, novel adsorbents are developed. To evaluate novel adsorbents for drying applications, we propose a simple two-step model-based method using equilibrium data and a dynamic process model. Based on a case study for an adsorption dishwasher, we determine the trade-off between adsorber unit size and drying times for Zeolite 13X, Silica Gel 125, SWS-1L and SBA-15 E120. For the adsorption dishwasher, SWS-1L is found to be a very promising adsorbent. Employing SWS-1L instead of Zeolite 13X in an adsorption dishwasher reduces the amount of adsorbent by 50%. Even though the adsorbent mass is lower, the drying speed is still the fastest among the studied adsorbents. The proposed two-step model-based evaluation method enables the identification of promising adsorbents for adsorptive drying systems.

Experimental vapor-liquid equilibrium data are of major importance for the chemical industry. However, the measurement of vapor-liquid equilibrium data still requires significant experimental effort. Therefore, we present a novel setup and measurement procedure for the rapid determination of isothermal vapor-liquid equilibria using only milliliter samples. The compositions of both liquid and vapor phases are analyzed using Raman spectroscopy. The measurement setup is successfully validated by reproducing vapor pressures of four pure substances and binary vapor-liquid equilibrium data of methyl tert -butyl ether (MTBE) and 2,2,4-trimethylpentane ( iso -octane) at T = 318.1 K as test system from literature. The vapor pressures of MTBE, ethanol, iso -octane and toluene agree with literature data within the measurement uncertainties. The measured binary vapor-liquid equilibrium data are modeled using the PCP-SAFT equation of state. Phase equilibrium data calculated from PCP-SAFT are compared to experimental data from literature. The data deviate by less than 1% in terms of pressure and vapor phase composition for given temperature and liquid phase composition demonstrating the reliability of the presented setup for vapor-liquid equilibrium measurements.

Property predictions are essential when dealing with molecules that have not been investigated experimentally yet. The accuracy of current predictive models like predictive perturbed-chain polar statistical associating fluid theory (PCP-SAFT) and conductor-like screening model for real solvents (COSMO-RS) is limited. We propose a combination of predictive models in order to yield a higher accuracy. Information from both predictive models are combined in PCP-SAFT parameter space using a log-likelihood function. Experimental vapor pressures, enthalpies of vaporization, and liquid densities over a wide temperature range are used to evaluate the predictions. The average error in the combined property prediction is lower than the error of the individual models. In addition, the maximum error is considerably lowered.

Carbon dioxide (CO2) and hydrogen are promising feedstocks for a sustainable chemical industry. Currently, the conversions of CO2 and hydrogen are most advanced for chemicals with 1 carbon atom, the so-called C1-chemicals, with the first pilot plants in operation. For formic acid, carbon monoxide, methanol, and methane, CO2-based C1-chemicals can reduce the impacts of fossil depletion and global warming through the substitution of fossil-based processes. Existing life cycle assessment (LCA) studies for carbon monoxide, methanol, and methane show that a reduction in environmental impacts is achieved if hydrogen is supplied by water electrolysis with renewable electricity. However, in the foreseeable future, renewable electricity will be limited. Thus, from an environmental point of view, renewable electricity should be employed for chemical processes in the order of highest environmental impact reductions. Environmental impact reductions are the difference in environmental impacts of fossil-based processes and CO2-based processes. In this study, we compared the CO2-based production of formic acid, carbon monoxide, methanol, and methane. We determined the reduction of global warming and fossil depletion impacts using 1 kg of hydrogen. Our results show that the CO2-based production of formic acid achieves the highest environmental impact reductions, followed by carbon monoxide and methanol. The lowest environmental impact reductions are achieved for CO2-based methane production. Our analysis reveals that the CO2-based production of formic acid can reduce environmental impacts, compared to the fossil-based process, even if hydrogen is supplied by fossil-based steam-methane-reforming.

Thermal cracking of methane into carbon and hydrogen is considered as potential hydrogen production technology without direct CO 2 -emissions. In this work, a novel methane-cracking process based on a liquid-metal technology is analyzed using life cycle assessment to evaluate the process' environmental impacts. Based on lab-scale experimental data, the novel methane-cracking process is benchmarked against the existing hydrogen production routes: steam reforming and water electrolysis. We consider the following environmental impact categories: global warming, fossil depletion, metal depletion, and particulate matter formation. According to our analysis, the methane-cracking process can reduce the global warming impact by up to 64% compared to steam reforming. However, the fossil depletion impact is higher for the methane-cracking process due to the higher methane input. The fossil depletion impact can be reduced by utilizing the energy of co-produced carbon to increase process efficiency at the expense of additional CO 2 -emissions. Methane supply to the process and electricity demand for H 2 -separation were identified as crucial parameters for the process’ environmental impacts. Thus, we perform parameter studies on alternatives for supply of methane and electricity to identify locations where lowest environmental impacts can be achieved.

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.

Crosslinked poly-N-isopropylacrylamide (pNIPAM) gels adapt to their environment by a unique transition from a flexible, swollen macromolecular network to a collapsed particle. pNIPAM gels are swollen in both, pure water and pure methanol (MeOH). However, a drastic volume loss is observed in mixtures of water and methanol over a wide composition range. This effect is referred to as cononsolvency. Cononsolvency couples the volume phase transition to the transport of the cosolvent into the polymeric network. So far, the mechanisms underlying cononsolvency have not been fully elucidated. To obtain insights on cononsolvency, Raman microspectroscopy was applied to capture spatially resolved spectra distinguishing between the surroundings and the inside of the gel. Here, we used Indirect Hard Modelling (IHM) for the spectral analysis. Mass balancing allowed the calculation of the solvent composition inside the pNIPAM gel. The results show an increased methanol fraction inside the collapsed gel as compared to its surroundings. Furthermore, the sensitivity of the vibrational bands of methanol to its local hydrogen bonding environment allow to derive information about the molecular interactions. The methanol peak shifts measured inside the gel point towards donor-type hydrogen bonds between methanol and the peptide group of pNIPAM in the cononsolvency-induced collapse. The presented data should enhance our understanding of cononsolvency.

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...

We present a method to calibrate Raman measurements of reactive systems without relying on thermodynamic models. Raman spectroscopy is able to detect multiple species even in reactive mixtures. Classical calibration methods require the knowledge of concentrations of all species as input. Typically, concentrations of intermediates cannot be prepared and fixed independently and are therefore calculated using thermodynamic models. The quality of the results thus depends on the accuracy of the thermodynamic model. In this paper, we present a method based only on stoichiometric balances and electroneutrality. By avoiding the use of thermodynamic models in the calibration step, the risk of overfitting spectroscopic data to a thermodynamic model is avoided. The presented method is demonstrated for the reactive system monoethanolamine, water, and CO2 and is validated by a thermodynamic model taken from the literature. While the model-free calibration is demonstrated for Raman spectroscopy in this work, the approach is generic should thus be applicable to most spectroscopic techniques.

Power-to-Gas enables the integration of renewable electricity and carbon into the chemical industry. The electricity is used to produce hydrogen, which is subsequently converted with CO2 as the renewable carbon source. The resulting products can be used as feedstock for the chemical industry replacing current fossil-based feedstock. Because the integration of renewable electricity and carbon into the chemical industry is mainly environmentally motivated, we identify the conditions under which Power-to-Gas pathways are environmentally beneficial. The conditions are expressed as environmental threshold values for electricity supply. The threshold values are derived by a comparative life cycle assessment (LCA) of Power-to-Gas pathways to fossil-based processes. We analyze Power-to-Gas pathways to synthetic natural gas (Power-to-SNG) and to syngas (Power-to-Syngas). SNG is produced by the Sabatier reaction; syngas by reverse water gas shift (rWGS) and dry reforming of methane (DRM). The threshold values for e...

Statistical Associating Fluid Theory (SAFT) equations of state (EoS) for mixtures require cross-interaction parameters. For real systems, combining rules, such as the Lorenz-Berthelot combining rules, have to be corrected using at least one binary interaction parameter, kij. Values of kij are usually adjusted to experimental data of phase equilibria. Here, we correlate kij to the pure component parameters of the Perturbed Chain – Statistical Associating Fluid Theory (PC-SAFT) EoS, using a Quantitative Structure Property Relationship (QSPR) model. The coefficients of the proposed QSPR model are regressed separately for mixtures with non-associating components and for mixtures with associating components. The QSPR model is validated using the statistical measures of the QSPR method. We compare the values of kij that are estimated from the QSPR model to values of kij estimated from London's dispersive theory. Phase equilibrium calculations carried out with these two approaches of estimating kij values are compared to experimental data. The estimation of kij values as function of the pure component PC-SAFT parameters can be applied to problems of process design and in Computer Aided Molecular Design (CAMD), to allow for calculations that are reasonably accurate and independent from the availability of experimental mixture data.

Chemical absorption of CO2 using aqueous amine-based solvents is one of the common approaches to control acidic gases emissions to the atmosphere. Improvement in the efficiency of industrial processes requires precise monitoring tools that fit with the specific application. Process monitoring using in-line multivariate measurement methods provides access to time resolved data of the reaction progress and the composition of the reaction mixture. Fast acquisition of valuable information about the process on site can be used for automated monitoring and process control, saving operational costs and reducing waste products. Therefore, different analytical techniques are being explored for the purpose of their practical application in process analysis. This work compares three vibrational spectroscopy techniques for monitoring CO2 absorption by aqueous monoethanolamine (MEA) solutions: Raman spectroscopy, near infra-red (NIR) spectroscopy, and attenuated total reflectance Fourier transform infra-red (ATR FTIR) spectroscopy. The spectroscopic information has been used to estimate the concentration of CO2 captured by the chemical solvent. The study aims to determine the potential applicability of the spectroscopic methods to the in-line and real time monitoring of a post-combustion capture process. Partial least squares (PLS) regression models were built based on the spectroscopic data before and after spectra pretreatment procedures. All three spectroscopic methods are shown to be well suited. The estimation model constructed using NIR data provides the highest accuracy for estimation of the CO2 loading, with an average deviation of about 0.01 molCO2/molMEA. The models based on Raman and ATR FTIR measurements show deviations of around 0.02 molCO2/molMEA.

Integrating fluctuating renewable energy into continuously operating industries requires energy storage. Energy storage can be achieved by using hydrogen from fluctuating, renewable energy for hydrogenation of CO2. The resulting molecule serves as storage. The simplest molecule that can be stored in liquid form is formic acid. Stored formic acid can then be reformed continuously to carbon monoxide, a common feedstock for the chemical industry. Since formic-acid synthesis is thermodynamically challenging, we investigate alternative storage molecules such as formamides or formates. Currently, it is unknown which storage molecule leads to the most efficient storage process. Thus, we systematically identify the most efficient storage molecule, together with an optimal combination of solvent and process flowsheet. We identify this combination with a novel hierarchical model-based approach, which starts by screening with the predictive thermodynamic model COSMO-RS and ends by using experimental property data. In the novel approach, we evaluate more than 100 000 combinations of storage molecules, solvents and process flowsheets. The most efficient combination identified uses the storage molecule N,N-dimethylformamide, and reduces the exergy loss by more than a factor of 15 compared to storage of formic acid, and still 65% compared to a literature benchmark. The largely reduced exergy loss indicates an environmentally promising route for linking fluctuating, renewable energy with continuously operating chemical industries. Our findings therefore highlight the importance of catalyst development for N,N-dimethylformamide in the optimal solvents.

Water quality and socioeconomics influence caries in populations. This study broadens previous studies on how caries is associated with fluoride and calcium in drinking water and with family income by quantifying the combined effect of the three independent variables. The effects of calcium and fluoride can be described as independent effects of the two ions or, alternatively, in the form of saturation with respect to fluorite (CaF2). A general linear model describes this relationship with high significance and the model confirms the important protective effect of calcium and fluoride, independently against caries. From the model, the relative importance of fluoride and calcium to protect against caries is quantified. The relationship between caries and family income is also highly significant. It is illustrated how the linear model can be applied in planning and analyzing drinking water softening in relation to caries.

Sustainable energy & fuels 2(6), 1153-1169 (2018). doi:10.1039/C8SE00061A, Published by Royal Society of Chemistry, Cambridge

Introduction. Management of phenylketonuria (PKU) is achieved through low-phenylalanine (Phe) diet, supplemented with low-protein food and mixture of free-synthetic (FS) amino acid (AA). Casein glycomacropeptide (CGMP) is a natural peptide released in whey during cheese-making and does not contain Phe. Lacprodan® CGMP-20 used in this study contained a small amount of Phe due to minor presence of other proteins/peptides.Objective. The purpose of this study was to compare absorption of CGMP-20 to FSAA with the aim of evaluating short-term effects on plasma AAs as well as biomarkers related to food intake.Methods. This study included 8 patients, who had four visits and tested four drink mixtures (DM1–4), consisting of CGMP, FSAA, or a combination. Plasma blood samples were collected at baseline, 15, 30, 60, 120, and 240 minutes (min) after the meal. AA profiles and ghrelin were determined 6 times, while surrogate biomarkers were determined at baseline and 240 min. A visual analogue scale (VAS) was used for evaluation of taste and satiety.Results. The surrogate biomarker concentrations and VAS scores for satiety and taste were nonsignificant between the four DMs, and there were only few significant results for AA profiles (not Phe).Conclusion. CGMP and FSAA had the overall same nonsignificant short-term effect on biomarkers, including Phe. This combination of FSAA and CGMP is a suitable supplement for PKU patients.

CO2 conversion covers a wide range of possible application areas from fuels to bulk and commodity chemicals and even to specialty products with biological activity such as pharmaceuticals. In the present review, we discuss selected examples in these areas in a combined analysis of the state-of-the-art of synthetic methodologies and processes with their life cycle assessment. Thereby, we attempted to assess the potential to reduce the environmental footprint in these application fields relative to the current petrochemical value chain. This analysis and discussion differs significantly from a viewpoint on CO2 utilization as a measure for global CO2 mitigation. Whereas the latter focuses on reducing the end-of-pipe problem “CO2 emissions” from todays’ industries, the approach taken here tries to identify opportunities by exploiting a novel feedstock that avoids the utilization of fossil resource in transition toward more sustainable future production. Thus, the motivation to develop CO2-based chemistry does...

Vapor–liquid equilibria are investigated for two promising biofuel blends: 2-methylfuran (2-MF) + iso-octane and 2-methyltetrahydrofuran (2-MTHF) + di-n-butyl ether (DNBE). Vapor–liquid equilibria of fuels are of major importance as critical factor for the air–fuel mixture formation in a combustion engine. We collected bubble-point pressures of these two binary biofuel blends. The experiments were conducted in the temperature range of 353–508 K at pressures of up to 3.7 MPa using a visual synthetic method (Cailletet method). A slight positive deviation from ideal behavior was found for the mixture of 2-MF + iso-octane. The extent of the deviation increases with decreasing temperature. A more pronounced negative deviation from ideal behavior was found for the mixture 2-MTHF + DNBE. The extent of the negative deviation increases with increasing temperature. For correlation of the experimental data, the PCP-SAFT equation of state was used in combination with one-fluid mixing rules, using a binary interaction parameter for the dispersion interaction. It was shown that PCP-SAFT correctly predicts the mixture behavior from pure component data only.

Solvent-based separation systems have a substantial potential for improvement when the solvent and the process conditions are optimized simultaneously. The fully integrated design problem, however, leads to an optimization problem of prohibitive size and complexity owing to the many discrete degrees of freedom in selecting a solvent and the nonlinear nature of the process models. We here implement and extend the method of continuous molecular targeting–computer-aided molecular design (CoMT–CAMD) for the solvent and process optimization of a precombustion CO2-capture system with physical absorption. CoMT–CAMD is a deterministic procedure that does not require a preselection of solvent molecules. The process topology considered in our study includes all major process operations of an existing CO2-capture system: multistage absorption, desorption (two flash desorption stages with gas recycle) and CO2 compression. We measure the process performance with a single economic objective function. The objective func...

CO2 readily reacts in aqueous amine solutions. The properties of free CO2 in amine solutions are therefore difficult to obtain directly and are often predicted from the nonreacting molecule N2O due to the similarities in mass and structure. This often-used empirical "CO2/N2O analogy" is verified in this work using molecular simulation. Continuous fractional component Monte Carlo (CFCMC) simulations in the osmotic ensemble were used to compute the Henry coefficients of CO2 and N2O in the solvents water, ethanol, n-heptane, and a 30% aqueous MEA solution at a temperature of 303 K. Molecular dynamics (MD) simulations were performed to compute the self-diffusivities of CO2 and N2O in the aforementioned solvents at 303 K. Different force fields for CO2 and water were used. The computed Henry coefficients and self-diffusivities of CO2 and N2O in the solvents are in good agreement with available experimental data. The simulation results indicate that the CO2/N2O analogy is valid for aqueous MEA solution at 303 K. The Henry coefficient and self-diffusivity ratios of CO2 to N2O in water and 30% MEA solution are approximately 0.77 and 1.1, respectively. Additional simulations where all the amines have reacted with CO2 confirm that reactions have little impact on the physical absorption and diffusion properties of CO2.

Diffusion is slow. Thus, diffusion experiments are intrinsically time-consuming and laborious. Additionally, the experimental effort is multiplied for multicomponent systems as the determination of multicomponent diffusion coefficients typically requires several experiments. To reduce the experimental effort, we present the first microfluidic diffusion measurement method for multicomponent liquid systems. The measurement setup combines a microfluidic chip with Raman microspectroscopy. Excellent agreement between experimental results and literature data is achieved for the binary system cyclohexane + toluene and the ternary system 1-propanol + 1-chlorobutane + heptane. The Fick diffusion coefficients are obtained from fitting a multicomponent convection-diffusion model to the mole fractions measured in experiments. Ternary diffusion coefficients can be obtained from a single experiment; high accuracy is already obtained from two experiments. Advantages of the presented measurement method are thus short measurement times, reduced sample consumption, and less experiments for the determination of a multicomponent diffusion coefficient.

International journal of refrigeration 77, 39-47 (2017). doi:10.1016/j.ijrefrig.2017.02.025, Published by Elsevier Science, Amsterdam [u.a.]

Chlorine and caustic soda are two indispensable chemical commodities co-produced in the so-called chlor-alkali electrolysis. Chlor-alkali electrolysis is today a target for cleaner production because of its large electricity demand causing considerable environmental impacts. The electricity demand of chlor-alkali electrolysis can be reduced by 30% using oxygen depolarized cathodes (ODCs) instead of the standard cathodes (STCs) used today. However, ODCs require additional resources and do not produce hydrogen in contrast to existing chlor-alkali plants. This work investigates if the reduction in electricity demand also contributes to cleaner production. For this purpose, environmental impacts from chlor-alkali electrolysis using ODCs are compared to the impacts from best available existing chlor-alkali plants using STCs. The life cycle assessment includes manufacturing, operation and disposal of the plants. To account for utilization of hydrogen from existing chlor-alkali plants, two alternative utilization scenarios are studied: energy recovery by combustion of hydrogen and use of hydrogen as chemical commodity. Seven environmental impact categories are studied in detail using the ReCiPe method. Chlor-alkali electrolysis using ODCs yields lower environmental impacts in up to six environmental impact categories. Plant operation contributes most in six out of seven impact categories. Chlor-alkali electrolysis using ODCs has thus potential to contribute to cleaner production in the chlor-alkali industry.