AI-powered insights into the UniSpray ionization in supercritical fluid chromatography-mass spectrometry

Selection of the optimal makeup solvent composition is critical for achieving sensitive and reproducible ionization in supercritical fluid chromatography-mass spectrometry (SFC-MS). This study investigated the ionization processes in a spray-based ionization source called UniSpray (US), by an artificial neural network driven approach, emphasizing the effect of makeup solvent composition. A set of compounds with different physicochemical properties was analyzed using a generic SFC method and 24 makeup solvents. Artificial neural networks were used to correlate molecular descriptors with MS responses and to identify key analyte properties affecting ionization. Statistical analysis of this extensive dataset revealed significant differences in ionization efficiency compared to electrospray ionization (ESI), depending on makeup solvent composition and analyte properties. While US outperformed ESI for 82 % of compounds, certain analytes, including basic beta-blockers, fluorine-substituted compounds, and small lipophilic molecules, benefited from ESI. Optimized makeup solvent compositions differed notably between ESI and US. For example, ethanol and isopropanol were recommended for US⁺ but not for ESI⁺. The use of water and ammonia also affected MS responses differently between sources and ionization modes, with optimal concentrations varying depending on the analyte and organic modifier of the SFC mobile phase. This study highlights key differences between SFC-ESI-MS and SFC-US-MS ionization efficiency and demonstrates the utility of data-driven methodologies for faster and more efficient method development.