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

A multiscale framework for developing equations of state of supercritical aqueous systems from synchrotron experiments, atomistic simulations, and machine learning | Research Square Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A multiscale framework for developing equations of state of supercritical aqueous systems from synchrotron experiments, atomistic simulations, and machine learning Konstantinos Karalis, Sergey V. Churakov, Nikolaos Prasianakis, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9264106/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract 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. Materials Theory and Modeling Environmental Engineering Materials Chemistry Thermodynamics and statistical mechanics Full Text Additional Declarations The authors declare no competing interests. Supplementary Files supplementaryinformation.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information

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