A multiscale framework for developing equations of state of supercritical aqueous systems from synchrotron experiments, atomistic simulations, and machine learning

The increase of fossil fuels use is responsible for the increased greenhouse gas emissions which have a great impact on the environment leading to global warming. Consequently, substantial efforts are devoted to decrease our dependence on fossil fuels and there is a growing interest in harnessing the chemical energy from other carbon neutral sources like biomass. Supercritical water gasification (SCWG), which is a catalyst-mediated process, provides a promising method to transform organic streams and wastes into fuels due to the utilization of water as a non-toxic green solvent. However, the process is limited by sulfur impurities that deactivate the catalysts required for efficient conversion. Τhe optimization of SCWG remains challenging because the solvent properties of aqueous mixtures in the supercritical regime are unknown, and they have significant differences while crossing the Widom line, which separates liquid- and gas-like states. In this study, we propose a framework to determine accurate thermodynamic properties for complex systems under supercritical conditions, where experimental data are scarce. We demonstrate that a methodology combining structural characterization experiments, atomistic simulations, and machine learning enables the development of reliable equations of state for systems with previously unknown properties. This approach reveals how the Widom line governs the dissolving power of water and, in turn, controls sulfur speciation in SCWG. By enabling selective sulfur removal and preventing catalyst poisoning, our framework advances the SCWG method as a clean energy technology. More broadly, the methodology is generalizable and can be applied to a wide range of complex fluid systems with poorly understood thermodynamic behavior.