Experimentally determining interfacial tension (IFT) for compositions relevant to CO₂ transport is challenging. We address this using molecular dynamics (MD) simulations and perturbed-chain statistical associating fluid theory (PC-SAFT) equation of state with classical density functional theory. We compute phase equilibria and interfacial properties of pure CO₂ and CO₂–CH₄, CO₂–Ar, CO₂–N₂, and CO₂–H₂ mixtures at 220–273 K. Both approaches accurately estimate CO₂ phase equilibria and IFTs. For binary mixtures, phase equilibria computed using PC-SAFT agree well with experiments when k_ij ≠ 0. IFTs computed from PC-SAFT depend strongly on k_ij, while MD simulations systematically overpredict IFTs. The IFT decreases with increasing pressure, least pronouncedly for H₂-containing mixtures. Binary mixtures exhibit interfacial enrichment of the light boiling component, decreasing with increasing temperature and pressure. Semiempirical Parachor and Winterfeld–Scriven–Davis models capture IFT–pressure trends with mixture-dependent accuracy. These results improve predictions of metastable limits and provide key insights for fast-transient multiphase CO₂ flow modeling.

Acetic acid production from renewable processes such as biomass hydrolysis and electrochemical reduction of CO₂ exhibits low concentrations, which make downstream separation challenging. We measured the vapor–liquid equilibria of the binary systems acetic acid + 2-methyltetrahydrofuran (2-MTHF), methyl-t-butyl ether (MTBE) + acetic acid, and the ternary liquid–liquid equilibria of the system 2-MTHF + AA + water, fitted the data to the UNIQUAC-HOC and NRTL models, designed a hybrid extraction-distillation process for acetic acid separation with 2-MTHF, and evaluated its economics and compared with that of three other commonly used solvents (i.e., ethyl acetate, MTBE, and methyl propyl ketone). The lowest and highest costs of separation were observed for MTBE and MPK, while 2-MTHF and EA showed similar performance. The cost of separation increased exponentially as the feed concentration decreased, and renewable processes should aim for at least 5 wt % acetic acid in the feed to allow economically feasible separation.

We study the interactions of plasma-generated Reactive Oxygen and Nitrogen Species (RONS) with water due to their importance for applications in health and agriculture. Atomic oxygen, a key RONS, is produced by plasma in both its triplet ground state, O(³P), and its singlet excited state, O(¹D). Experimental studies indicate that when plasma interacts with water, atomic oxygen can remain sufficiently stable to enter the aqueous phase. Recent measurements show that ground-state oxygen atoms can persist for tens of microseconds and penetrate hundreds of micrometres into the aqueous phase. However, quantitative data on the solubility and diffusion of atomic oxygen remain scarce. This is likely due to limitations in experimental diagnostics and the challenges that the complex electronic structure of atomic oxygen presents to modeling approaches. To overcome these challenges, we developed state-specific force fields to model the interactions of O(³P) and O(¹D) with water to account for quantum-state-dependent interactions. Using these force fields, we provide the first estimates of temperature- and quantum-state-dependent self-diffusion and Henry coefficients of atomic oxygen in aqueous environments. Building upon these results, we propose a general framework to estimate the solubility and diffusion of other plasma-generated charge-neutral RONS in water by representing each species as a charge-neutral Lennard-Jones particle. The influence of particle size, solute–solvent interaction strength, and temperature on the transport and thermodynamic properties of RONS was systematically investigated. This approach enables the estimation of the Henry coefficients and the diffusion coefficients of RONS in water based on particle size, solute–solvent interactions, and temperature. These estimates provide key parameters for device-level plasma-liquid simulations and offer molecular-scale insight for interpreting experimental findings.

Hydroisomerization of alkane isomers is an important step in the manufacture of current kerosene and sustainable aviation fuels. Zeolites are used as acid catalysts in this process. It is therefore important to have predictions of the adsorption capacity or maximum loading of hydrocarbons in zeolites. Here, a cascade model using machine learning models is used to predict the maximum loading of alkane isomers in zeolites. The cascade is composed of a gradient-boosted tree classifier stage that predicts whether adsorption occurs and a regressor predicting the value of the maximum loading. The final data set consists of 45 different adsorbates (both linear and branched alkanes up to C₁₆) and 97 different zeolite structures, resulting in 4365 data points. Descriptors include information on the geometry and topology of zeolite channels as well as the shape and size of the adsorbates. Extra composite descriptors are also present to provide the physical basis for predictions. Multiple regressors of different natures are considered: support vector regressors, gradient-boosted trees, extreme gradient-boosted trees, and the TabPFN pretrained model. TabPFN yields the highest generalization performance and the lowest error. An interpretability analysis using SHAP reveals that the most influential descriptors are physically meaningful, highlighting steric and volumetric constraints as the primary factors controlling the prediction of q_max. It is shown that despite both the classifier and the regressor being insensitive to random splits in data, the regressor is prone to overfitting at low fractions of data withheld for testing. The cascade model is compared to an Artificial Neural Network for training and resource efficiency. Despite training being longer for the neural network, the final model is lighter in both memory and storage. This work is built on our previous research in predicting the Henry coefficients of long-chain alkanes in zeolites. Using this previous model and the findings of this work, one could construct the adsorption isotherm for any alkane, thus enabling the analysis of adsorption behavior of alkane mixtures using IAST.

Electrochemical CO₂ reduction to CO offers a sustainable route for converting CO₂ into value-added chemicals and fuels. However, CO₂ streams derived from industrial sources often contain SO₂ impurities that severely poison conventional metal-based catalysts. Here, we report a nitrogen-doped carbon catalyst that exhibits pronounced tolerance and stability for CO₂-to-CO conversion in the presence of SO₂ (100–10,000 ppm). The catalyst maintains over 90% Faradaic efficiency toward CO during 8 h of electrolysis at −1.0 V vs RHE with 100 ppm of SO₂, whereas Ag foil electrodes undergo rapid deactivation. Density functional theory calculations combined with surface analyses indicate that weak SO₂ adsorption and the absence of stable sulfur accumulation on nitrogen-doped carbon strengthen its resistance to impurity-induced deactivation, in contrast to Ag catalysts that form Ag₂S. Gas-fed tests in a membrane electrode assembly (MEA) electrolyzer further confirm that nitrogen-doped carbon sustains high CO selectivity at elevated current densities, while Ag nanoparticles suffer irreversible sulfur poisoning. These results demonstrate that nitrogen-doped carbon is intrinsically resistant to SO₂-induced deactivation and highlight its potential as a robust catalyst for CO₂ electroreduction under impurity-containing conditions.

We introduce OpenPyTEA , an open-source Python toolkit for conducting flexible and detailed techno-economic assessments (TEA) of chemical and energy systems. TEA is essential for evaluating economic feasibility in process design, yet commercial tools are “black-box” solutions with limited flexibility. Existing open-source options are usually process-specific, incomplete, or poorly documented, limiting reproducibility and cross-study comparisons. OpenPyTEA addresses these challenges by integrating equipment cost estimation, cash-flow analysis, and sensitivity and uncertainty methods into transparent and adaptable workflows. Its capabilities are demonstrated through a case study comparing hydrogen production via steam methane reforming, methane pyrolysis, and water electrolysis.

To enhance the efficiency of thermodynamic cycles in heat pumps and power plants, we explore a novel approach: replacing conventional inert pure fluids or mixtures with reactive fluids that undergo reversible chemical reactions. A key step towards the implementation of this concept is the development of a fully predictive framework for determining the thermodynamic properties of such reactive working fluids. In this context, the present work extends a semi-empirical methodology previously proposed by the authors, aiming to address the challenge introduced by newly developed reactive fluids for which experimental data are unavailable. The methodology presented in this work requires only the critical-point properties and acentric factor of the molecules participating in the chemical reaction. As in the earlier approach from the authors, it combines ab-initio quantum mechanics calculations to determine the ideal gas properties of each molecule, the a-thermal version of the “Peng-Robinson + EoS/a_res^E,γ mixing rules” equation of state and molecular Monte Carlo simulations to assess real fluid properties and enable cross-validation between methods. This work, however, applies a simplification to the force fields used in Monte Carlo simulations consisting in employing single-particle force fields instead of all-atom models. This strategy decreases the amount of experimental data required to parametrise the force field of each molecule contained in the reactive mixture, and allows the use of the same inputs in equation of state modelling and Monte Carlo simulations (i.e., molecular critical parameters). Indeed, this work proposes to calculate force field parameters using either the critical temperature and pressure, or the critical temperature and density of each molecule. The methodology is applied to two reactive systems, Al₂Br₆ ⇌ 2AlBr₃ and Al₂Cl₆ ⇌ 2AlCl₃. The results show that Monte Carlo predictions, although less accurate than those from the equation of state, remain acceptably close to experimental data, while the equation of state results demonstrate significantly higher accuracy.

Hydrogen can play a central role in a fossil-free energy economy, yet its implementation is hindered by the lack of safe, dense, and efficient storage methods. Hybrid H₂ physisorption-hydrate formation, which combines physisorption in porous materials with encapsulation in clathrate hydrates, presents a promising route, but the fundamental synergistic mechanisms remain largely elusive. Here, we perform microsecond-scale molecular dynamics simulations to study the hybrid H₂ storage process in the hydrophobic metal–organic framework ZIF-8 seeded with THF hydrate nanoparticles. The results indicate that ZIF-8 rapidly physisorbs H₂, while effectively excluding H₂O and THF. Our simulations reveal a dynamic, three-step hybrid storage pathway, i.e. , (1) ZIF-8 selectively adsorbs and enriches H₂ within its pores, creating a high local H₂ concentration; (2) The growing binary H₂-THF hydrate crystals selectively capture the H₂; (3) Transfer of H₂ from the ZIF-8 to the hydrate until the hydrogen source transfer reaches a dynamic equilibrium. This hybrid storage method results in a total H₂ storage capacity reaching 1.82 wt%, exceeding the storage capacity of either physisorption or THF-driven hydrate formation alone. These findings provide critical molecular-level insights, showing that coupling hydrophobic ZIF-8 with hydrate promoters is a highly effective strategy for developing next-generation H₂ storage methods.

Knowledge of the effect of different organic molecules on spontaneous hydrate nucleation is crucial for understanding the formation of gas hydrates in marine reservoirs. Herein, microsecond MD simulations are conducted to investigate the spontaneous nucleation of CH₄ hydrates in oceanic sediments. The simulation results indicate that hydrate nucleation is influenced by the coupling effects of organic molecules, clay surfaces and salt ions, where organic molecules alter hydrate nucleation by modulating the diffusion fluctuation of CH₄ molecules via controlling the shape and size of CH₄ nanobubbles. Furthermore, CH₄ hydrates are primarily concentrated at a moderate distance away from the nanobubbles, with fewer hydrates located either close or at a more distant from the nanobubbles. In the region about 1.0 nm away from the nanobubbles, the hydrates become more unstable when closer to the nanobubbles, whereas hydrates have better stability when locating above 1.0 nm away from the nanobubbles. Different organic molecules exert distinct effects on spontaneous hydrate nucleation. Specifically, propanol adsorbed to the nanobubble surface kinetically promotes hydrate nucleation, exhibiting a distinct advantage over other organic molecules. These molecular insights expand the understanding of the formation of natural gas hydrate resources and help to effectively utilize this resource.

We present a simulation-based framework to characterize the solvation and aqueous-phase reactivity of nitric oxide (NO) and nitrogen dioxide (NO2) in water. Using Continuous Fractional Component Monte Carlo (CFCMC) simulations, we compute Henry coefficients and chemical potentials of NO and NO2, while molecular dynamics (MD) simulations provide diffusion coefficients for NO. The results for NO are quantitatively in agreement with the experimental data when using the Saji force field. For NO2, we model the chemical equilibrium involving hydrolysis and acid-base reactions that generate HNO2, HNO3, NO2-, NO3-, and H3O+. By combining the chemical potentials obtained via CFCMC with a thermodynamic equilibrium model, we resolve the temperature- and pressure-dependent speciation and pH of the system. The model captures a transition from nitrous to nitric species with increasing temperature and predicts ionic distributions and pH shifts under varying NOx gas fluxes. This work provides a transferable methodology to connect molecular simulations with chemical speciation in reactive aqueous systems.

Grotthuss transfer is responsible for a large increase in the self-diffusion of hydroxide and hydronium ions in aqueous solutions compared to similarly sized ions. Recent advances in machine-learning molecular dynamics have shown some success in capturing this process. In the present work, we show that classical molecular dynamics combined with experimentally measured electrical conductivities can also be used to determine self-diffusion coefficients and the lifetimes of hydroxide and hydronium ions in aqueous KOH, NaOH, and HCl solutions. This was tested and validated across a wide range of concentrations at 25 and 60 °C. The approach relies on augmenting classically computed trajectories with a biased random walk, which together accounts for both vehicular transport and Grotthuss transfer. The concentration and temperature dependence of this random walk are calibrated by comparing simulated electrical conductivities with available experimental electrical conductivity data. The computed self-diffusion coefficients match measurements at infinite dilution and results from machine learning molecular dynamics. Ion lifetimes reported by machine learning and ab initio molecular dynamics studies depend strongly on the precise definition of what constitutes a Grotthuss transfer event. Our approach for calculating ion lifetimes does not have this drawback. We also show that our self-diffusion coefficients and electrical conductivities are insensitive to the precise definition of what constitutes a Grotthuss transfer event.

Computation of the excess entropy (Formula presented.) from the second-order density expansion of the entropy holds strictly for infinite systems in the limit of small densities. For the reliable and efficient computation of (Formula presented.) it is important to understand finite-size effects. Here, expressions to compute (Formula presented.) and Kirkwood–Buff (KB) integrals by integrating the Radial Distribution Function (RDF) in a finite volume are derived, from which (Formula presented.) and KB integrals in the thermodynamic limit are obtained. The scaling of these integrals with system size is studied. We show that the integrals of (Formula presented.) converge faster than KB integrals. We compute (Formula presented.) from Monte Carlo simulations using the Wang–Ramírez–Dobnikar–Frenkel pair interaction potential by thermodynamic integration and by integration of the RDF. We show that (Formula presented.) computed by integrating the RDF is identical to that of (Formula presented.) computed from thermodynamic integration at low densities, provided the RDF is extrapolated to the thermodynamic limit. At higher densities, differences up to (Formula presented.) are observed.

Atmospheric water harvesting (AWH) is a method to obtain clean water in remote or underdeveloped regions including, but not limited to, those with an arid or desert climate. For passive (i.e., relying on ambient cooling and, for heating, natural sunlight─as opposed to an external power source), adsorbent-based AWH, an adsorbent bed is employed to capture water from cold, humid air at nighttime, while during the daytime the bed is then exposed to natural sunlight to heat it and desorb the water for collection. Metal–organic frameworks (MOFs) are tunable, nanoporous materials with suitable water adsorption properties for comprising this adsorbent bed. The water delivery by the MOF adsorbent bed in a passive AWH device depends on (1) the nighttime, capture conditions (temperature and humidity) and daytime, release conditions (temperature, humidity, and solar flux) and (2) the structure(s) of the MOF(s) comprising the bed, which dictate MOF-water interactions. Notably, the capture and release conditions vary from region-to-region and season-to-season and fluctuate from day-to-day, while different MOFs offer different water adsorption isotherms. Consequently, we propose (1) comprising the adsorbent bed for passive AWH with a mixture of MOFs and (2) tailoring this MOF mixture to particular geographic regions and time frames. We hypothesize each MOF in the mixture can specialize in delivering water under different capture and release conditions, ensuring the adsorbent bed delivers adequate water on every day─despite fluctuations in temperature, humidity, and solar flux. Herein, we develop an optimization framework to determine the total mass and composition of a MOF mixture for comprising a bespoke (i.e., tailored to a declared geographic region and time frame) adsorbent bed for robust (i.e., delivering adequate water every day) passive AWH. We combine weather data in the declared region, equilibrium water adsorption data in the candidate MOFs, and thermodynamic water adsorption models (as a simplifying assumption, we neglect heat and water transfer limitations) to frame a linear program expressing our optimal design principle: adjust the mass of each candidate MOF comprising the adsorbent bed to minimize mass (important for portability and a proxy for cost) while satisfying daily water delivery constraints. Based on case studies in the Chihuahuan and Sonoran Deserts, we find (1) a mixed-MOF adsorbent bed can be, but is not always, lighter (e.g., ≈40% lighter) than the optimized single-MOF counterpart; and (2) the optimal composition and mass of the adsorbent bed differ by both geographic region and time frame. Finally, we visualize the linear program for a reduced problem with a two-dimensional design space to gain intuition, conduct a sensitivity analysis, and compare to an AWH field study. Our work is a starting point for optimizing the composition of bespoke adsorbent beds for robust, passive AWH.

Porous materials such as zeolites and Metal-Organic Frameworks are widely used for molecular separations based on adsorption and enthalpy/entropy characteristics. Ideal adsorption solution theory (IAST) predicts mixture adsorption behaviour on the basis of pure component isotherms of adsorbents in porous media. Mixture data at all mole fractions are required for breakthrough simulations. The use of IAST avoids the expensive computations of mixtures with Monte Carlo methods. Matching outcomes from computational physics studies to experimentally measurable properties is the foundation of the materials design pipeline. Here, we report the regression of an Invertible Autoencoder (IAE) for the forward and backward mapping of pure and mixture isotherms. The invertible autoencoder is defined as a soft-invertible neural network, which can be used as mapping function. Pure component isotherms are modelled using a 3-site Langmuir-Freundlich model, with a broad range of equilibrium pressure and heterogeneity factors. A synthetic dataset is generated from pure component isotherms and mixture isotherms calculated with RUPTURA. The IAE predicts pure and mixture isotherms with high precision over a large fugacity range, for up to 6 components and 3-site isotherms. This work contributes to inverting the full design pipeline from physical gas separation to adsorbate design, enabling property-guided materials discovery.

In the days prior to the Thermodynamics2024 conference in Delft (The Netherlands), the annual RASPA workshop/school took place at Delft University of Technology with 55 participants (both industry and academics) from all over the world. RASPA is a popular open-source molecular simulation software package for studying adsorption and diffusion in fluids and nanoporous materials, and it is especially popular in the metal-organic frameworks and zeolite communities. The main contributors to RASPA (and organisers of the RASPA workshop/school in Delft) are David Dubbeldam, Sofia Calero, Randall Q. Snurr, and Thijs J.H. Vlugt. In this short paper, we briefly explain the history of RASPA and the RASPA workshops, as well as our strategy to teach the workshop participants how to use RASPA for their specific research projects.

Cyclopentyl methyl ether (CPME) is a promising green solvent due to its eco-friendly properties; it is produced by adding methanol (MeOH) to cyclopentene. Separation of the resulting product mixture containing CPME and MeOH is critical, and it requires vapor-liquid equilibrium (VLE) data. In this work, isobaric VLE data were measured experimentally using an ebulliometer in a 60.0–101.3 kPa pressure range for a binary system of CPME + MeOH. VLE data were modeled using excess Gibbs (G (Formula presented.)) energy-based models such as Wilson, NRTL, and UNIQUAC. The formation of an azeotrope was analyzed. Flash point, surface tension, Gibbs adsorption, and thickness of surface layer were estimated using the Wilson model, which can help in determining molecule interaction and overall behavior of the system. Atmospheric and high-pressure distillation columns were designed using Aspen Plus to study the separation of CPME + MeOH, and an economic evaluation of the same was carried out.

Accurate prediction of thermodynamic properties of hydrocarbons is essential for chemical process modelling. Conventional group contribution methods often are used to predict these properties. However, these methods often require extensive parameter sets to handle structural complexities. A refined group contribution method for predicting thermodynamic properties of hydrocarbon isomers with reduced complexity and improved accuracy is presented and discussed. By combining the structural framework of Constantinou and Gani (CG94) with a sensitivity-based selection of second-order groups, a reduced yet highly effective set of twelve second-order groups is identified. This reduced set retains the predictive power comparable to more complex models while significantly reducing the number of parameters. Linear regression is applied to model enthalpies and Gibbs free energies of formation for a wide temperature range. To test broader applicability, the model is further extended to properties that require nonlinear regression, including critical temperatures, critical pressures, acentric factors, and liquid densities. For all cases, the proposed model achieves high predictive accuracy, demonstrating its robustness and generalizability. This methodology balances interpretability, efficiency, and performance, making it suitable for both research and industrial thermodynamic modelling.