Phasepy: A Python based framework for fluid phase equilibria and interfacial properties computation

Integrating molecular dynamics simulations and experimental data for azeotrope predictions in binary mixtures

An azeotrope is a constant boiling point mixture, and its behavior is important for fluid separation processes. Predicting azeotropes from atomistic simulations is difficult due to the complexities and convergence problems of Monte Carlo and free-energy perturbation techniques. Here, we present a methodology for predicting the azeotropes of binary mixtures, which computes the compositional dependence of chemical potentials from molecular dynamics simulations using the S0 method and employs experimental boiling point and vaporization enthalpy data. Using this methodology, we reproduce the azeotropes, or lack thereof, in five case studies, including ethanol/water, ethanol/isooctane, methanol/water, hydrazine/water, and acetone/chloroform mixtures. We find that it is crucial to use the experimental boiling point and vaporization enthalpy for reliable azeotrope predictions, as empirical force fields are not accurate enough for these quantities. Finally, we use regular solution models to rationalize the azeotropes and reveal that they tend to form when the mixture components have similar boiling points and strong interactions.

Kirkwood-Buff Analysis of Binary and Ternary Systems Consisting of Alcohols (Methanol, Ethanol, 1-Propanol, and 2-Propanol), Water, and n-Hexane to Understand the Formation of Surfactant-Free Microemulsions

Surfactant-free microemulsion (SFME) represents a class of fluid mixtures that can form microheterogeneous structures without detergents, offering an environmentally benign alternative to traditional microemulsions. However, the formation mechanism is still elusive. This work applies the Kirkwood-Buff theory to mixtures of alcohols, water, and n-hexane to elucidate the SFME formation mechanism. To ensure robust calculation of the Kirkwood-Buff integrals (KBIs), we construct a data set of densities and excess free energies of binary and ternary systems. Multiple excess Gibbs free energy models are assessed against this data set to select the most suitable model reproducing the experimental results. In addition, we introduce statistical methods to determine the optimal polynomial order of the Redlich-Kister correlation for the excess volume data. We first validate our methodology in binary systems. Then, we extend the calculation method to ternary mixtures. The KBI calculation results reveal that the alcohol-hexane and water-hexane interactions do not significantly affect SFME formation. In contrast, the interplay among water-water, water-alcohol, and alcohol-alcohol interactions critically influences the ability of a liquid mixture to form SFME structures. SFME systems exhibit the facile formation of water aggregates enveloped by alcohols, whereas non-SFME systems demonstrate homogeneous alcohol/water droplets dispersed in an oil continuous medium.

Indirect Electrochemical Cooling: Model-Based Performance Analysis and Working Fluid Selection

The rising energy demand for cooling and heating requires efficient and sustainable technologies. Vapor-compression systems represent the state of the art but suffer from downscaling limits and maintenance needs. These disadvantages may be overcome by recently proposed electrochemical processes. However, their potential has not been explored systematically. This work quantifies the thermodynamic potential of an indirect electrochemical cooling process that replaces the vapor compressor of a standard refrigeration cycle with an electrochemical cell. An equilibrium-based process model evaluates the process performance of a working fluid, depending on its composition and temperatures in the process. After screening an extensive database for possible working fluids, an electrochemical cooling process is analyzed and optimized for the coefficient of performance (COP) to operate between two heat reservoirs at 20 °C (heat source) and 35 °C (heat sink). The majority of the investigated working fluids yield smaller or similar efficiencies than vapor-compression refrigeration, with COPs between 3.0 and 4.0. However, 35 promising working fluids that achieve higher efficiencies are identified with a COP up to 9.63, corresponding to 49% of Carnot. These working fluids are worthy of further investigation as their use in the electrochemical cooling process possibly outperforms standard vapor-compression refrigeration.

Artificial intelligence-driven design of fuel mixtures

High-performance fuel design is imperative to achieve cleaner burning and high-efficiency engine systems. We introduce a data-driven artificial intelligence (AI) framework to design liquid fuels exhibiting tailor-made properties for combustion engine applications to improve efficiency and lower carbon emissions. The fuel design approach is a constrained optimization task integrating two parts: (i) a deep learning (DL) model to predict the properties of pure components and mixtures and (ii) search algorithms to efficiently navigate in the chemical space. Our approach presents the mixture-hidden vector as a linear combination of each single component's vectors in each blend and incorporates it into the network architecture (the mixing operator (MO)). We demonstrate that the DL model exhibits similar accuracy as competing computational techniques in predicting the properties for pure components, while the search tool can generate multiple candidate fuel mixtures. The integrated framework was evaluated to showcase the design of high-octane and low-sooting tendency fuel that is subject to gasoline specification constraints. This AI fuel design methodology enables rapidly developing fuel formulations to optimize engine efficiency and lower emissions.

PyVaporation: A Python Package for Studying and Modelling Pervaporation Processes

PyVaporation-a freely available Python package with an open-source code for modelling and studying pervaporation processes-is introduced. The theoretical background of the solution, its applicability and limitations are discussed. The usability of the package is evaluated using various examples of working with and modelling experimental data. A general equation for the representation of a component's permeance as a function of feed composition, temperature and initial feed composition is proposed and implemented in the developed package. The suggested general permeance equation may be used for the description of an extremal character of permeance as a function of process temperature and feed composition, allowing the description of processes with a high degree of non-ideality. The application of the package allowed modelling experimental points of various sets of hydrophilic pervaporation data and data on membrane performance from independent sources with a relative root mean square deviation of not more than 9% for flux and not more than 5% for a separated mixture concentration. The application of the facilitated parameter approach allowed the prediction of the components' permeance as a function of feed concentration at various initial feed concentrations with a relative root mean square error of 3-26%. The package was proven useful for modelling isothermal and adiabatic time and length-dependent pervaporation processes. The comparison of the models obtained with PyVaporation with models provided in the literature indicated similar accuracy of the obtained results, thereby proving the applicability of the developed package.

Pressure–Surface Tension–Temperature Equation of State for n-Alkanes

Herein, the geometric similitude concept is applied to propose a cubic equation that relates surface tension, saturation pressure, and temperature for n-alkanes. The input properties for each fluid are the molecular mass, pressure, temperature, and compressibility factor at the critical point. The model is applied to temperatures below 0.93·T_c (critical point temperature). A total of 2429 surface tension values have been selected for 32 n-alkanes. The parameters of the model have been obtained with a fit of the surface tension values for 19 pure n-alkanes that are randomly chosen. Then, it is tested for the other 13 pure n-alkanes and used to predict the surface tension for 11 binary and 4 ternary mixtures. These predictions are compared with the reported experimental data. For pure n-alkanes, the overall absolute average deviation is 2.4%, including the correlation and testing sets. No additional adjustable coefficients are used for mixtures, yielding an overall absolute average deviation of 2.98% for the binary systems and 7.97% for the ternary ones. The results show that the model is accurate enough for predictions and that the highest deviations are due to the lack of agreement in the values of surface tension of pure fluids obtained from different sources.

Designing the bioproduction of Martian rocket propellant via a biotechnology-enabled in situ resource utilization strategy

Mars colonization demands technological advances to enable the return of humans to Earth. Shipping the propellant and oxygen for a return journey is not viable. Considering the gravitational and atmospheric differences between Mars and Earth, we propose bioproduction of a Mars-specific rocket propellant, 2,3-butanediol (2,3-BDO), from CO₂, sunlight and water on Mars via a biotechnology-enabled in situ resource utilization (bio-ISRU) strategy. Photosynthetic cyanobacteria convert Martian CO₂ into sugars that are upgraded by engineered Escherichia coli into 2,3-BDO. A state-of-the-art bio-ISRU for 2,3-BDO production uses 32% less power and requires a 2.8-fold higher payload mass than proposed chemical ISRU strategies, and generates 44 tons of excess oxygen to support colonization. Attainable, model-guided biological and materials optimizations result in an optimized bio-ISRU that uses 59% less power and has a 13% lower payload mass, while still generating 20 tons excess oxygen. Addressing the identified challenges will advance prospects for interplanetary space travel.

Returning from Mars to Earth requires propellant. The authors propose a biotechnology-enabled in situ resource utilization (bioISRU) process to produce a Mars specific rocket propellant, 2,3-butanediol, using cyanobacteria and engineered E. coli, with lower payload mass and energy usage compared to chemical ISRU strategies.

SGTPy: A Python Code for Calculating the Interfacial Properties of Fluids Based on the Square Gradient Theory Using the SAFT-VR Mie Equation of State

In this work, we showcase SGTPy, a Python open-source code developed to calculate interfacial properties (interfacial concentration profiles and interfacial or surface tension) for pure fluids and fluid mixtures. SGTPy employs the Square Gradient Theory (SGT) coupled to the Statistical Associating Fluid Theory of Variable Range employing a Mie potential (SAFT-VR-Mie). SGTPy uses standard Python numerical packages (i.e., NumPy, SciPy) and can be used under Jupyter notebooks. Its features are the calculation of phase stability, phase equilibria, interfacial properties, and the optimization of the SGT and SAFT parameters for vapor-liquid, liquid-liquid and vapor-liquid-liquid equilibria for pure fluids and multicomponent mixtures. Phase equilibrium calculations include two-phase and multiphase flash, bubble and dew points, and the tangent plane distance. For the computation of interfacial properties, SGTPy incorporates several options to solve the interfacial concentration, such as the path technique, an auxiliary time function, and orthogonal collocation. Additionally, the SGTPy code allows the inclusion of subroutines from other languages (e.g., Fortran, and C++) through Cython and f2py Python tools, which opens the possibility for future extensions or recycling tested and optimized subroutines from other codes. Supporting Information includes a review of the theoretical expressions required to couple SAFT-VR-Mie equation of state with the SGT. The use and capabilities of SGTPy are illustrated through step by step examples written on Jupyter notebooks for the cases of pure fluids and binary and ternary mixtures in bi- and three- phasic equilibria. The SGTPy code can be downloaded from https://github.com/gustavochm/SGTPy.