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The process of surrounding a solute with solvent is called solvation. It involves evenly distributing the solute within the solvent. The rule of thumb for determining a solvent for a given compound is that like dissolves like. A good solvent has molecular characteristics similar to those of the compound to be dissolved. For example, polar solutions dissolve polar solutes, and apolar solvents dissolve apolar solutes. A polar solvent is a solvent that has a high dielectric constant (ϵ...
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Supercritical fluid chromatography (SFC) provides a beneficial substitute for gas chromatography (GC) and liquid chromatography (LC) for certain samples because it merges the top attributes of both techniques. SFC allows the separation and analysis of compounds that GC or LC does not easily manage. These compounds are traditionally nonvolatile or thermally unstable, making GC unsuitable and lacking functional groups required for HPLC analysis.
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As for any solution, the solubility of a gas in a liquid is affected by the attractive intermolecular forces between solute and solvent species. Unlike solid and liquid solutes, however, there is no solute-solute intermolecular attraction to overcome when a gaseous solute dissolves in a liquid solvent since the atoms or molecules comprising a gas are far separated and experience negligible interactions. Consequently, solute-solvent interactions are the sole...
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Solubility equilibria are established when the dissolution and precipitation of a solute species occur at equal rates. These equilibria underlie many natural and technological processes, ranging from tooth decay to water purification. An understanding of the factors affecting compound solubility is, therefore, essential to the effective management of these processes. This section applies previously introduced equilibrium concepts and tools to systems involving dissolution and precipitation.
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The free energy change associated with dissolving a solute in a liter of solvent is called the free energy of a solution, ΔGsolution. The overall ΔGsolution is expressed as the balance of ΔGinteraction against the always-favorable free-energy of mixing, ΔGmixing. Solution formation is favorable if  ΔGsolution is less than zero, whereas it is unfavorable if ΔGsolution is greater than zero. In short, for a solution to form and complete dissolution to take place,...
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Related Experiment Video

Updated: May 13, 2025

Achieving Moderate Pressures in Sealed Vessels Using Dry Ice As a Solid CO2 Source
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Improved Solubility Predictions in scCO2 Using Thermodynamics-Informed Machine Learning Models.

Dmitriy M Makarov1, Nikolai N Kalikin1, Yury A Budkov1,2

  • 1Laboratory of Multiscale Modeling of Molecular Systems, G.A. Krestov Institute of Solution Chemistry of the Russian Academy of Sciences, Akademicheskaya Street, Ivanovo 153045, Russia.

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|April 15, 2025
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Summary

Predicting solubility in supercritical carbon dioxide (scCO2) is vital for efficient drug development. This study uses domain-aware machine learning with thermodynamic properties to improve solubility predictions, enhancing experimental design.

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Area of Science:

  • Computational Chemistry
  • Chemical Engineering
  • Machine Learning Applications

Background:

  • Accurate solubility prediction in supercritical carbon dioxide (scCO2) is essential for optimizing experimental design and reducing costs in chemical processes.
  • A comprehensive database of 31,975 solubility records has been compiled, supporting the development of predictive models for various chemical compounds, especially drug-like substances.

Purpose of the Study:

  • To develop and evaluate a domain-aware machine learning approach for predicting solubility in scCO2.
  • To incorporate thermodynamic properties governing phase transitions into solubility prediction models.
  • To compare the effectiveness of the CatBoost algorithm against a graph-based architecture with directed message passing.

Main Methods:

  • A domain-aware machine learning strategy was employed, integrating thermodynamic features relevant to phase transitions.
  • Predictive models were built using the CatBoost algorithm and a graph-based architecture with directed message passing.
  • Auxiliary solute properties such as melting point, critical parameters, enthalpy of vaporization, and Gibbs free energy of solvation were also predicted.

Main Results:

  • The study demonstrated the effectiveness of incorporating domain-specific thermodynamic features for enhancing scCO2 solubility prediction accuracy.
  • Both CatBoost and graph-based models showed promise, with domain-specific features significantly improving predictive performance.
  • Predicted auxiliary properties provided further insights into solute behavior in scCO2.

Conclusions:

  • Integrating domain-specific thermodynamic features significantly enhances the predictive accuracy of solubility in scCO2.
  • The developed models show good generalization capabilities, even for compounds outside the initial training domain.
  • This approach streamlines experimental design by providing reliable solubility predictions for drug-like substances and other compounds.