Abstract
A database is developed to facilitate correlations between surfactant structure, properties, and foam fire suppression and to develop machine learning models. Models and structure-property relationships are crucial for developing insights into surfactant design and development for a specific application such as firefighting. The database contains foam fire suppression metrics for 71 individual surfactants and 116 mixture formulations with well-characterized chemical structures. Thirty-six of the 71 are synthesized, and the rest are commercial. In addition, the database contains 13 commercial individual surfactants and 48 commercial mixture formulations without well-defined structures. Properties include surfactant critical micelle concentration, solution surface tension, and interfacial tension with fuels, fuel-induced foam degradation, heptane flux through a foam layer, and foam fire extinction performance on 19 cm heptane and gasoline pool fires. Limited acute aquatic toxicity measurements for 7 surfactants and 2 mixtures for 48 h exposure to Ceriodaphnia dubia are reported. These values were compared to the calculated acute aquatic toxicity for 48 h exposure to Daphnia magna using two methods (one being Ecological Structure Activity Relationships, ECOSAR). These programs were unable to address issues of surfactant polydispersity and showed inconsistencies in classifying surfactant toxicity. Both surfactant mixtures exhibited synergism in acute aquatic toxicity: Cap:G215 was more toxic and G225:502W was less toxic than their individual components. These calculated methods should not be used to assess the potential toxicity of surfactants within this database, and continued testing of surfactant mixture acute aquatic toxicity is needed. This database along with continued toxicity testing can contribute to model development for rapidly extinguishing, environmentally friendly firefighting foams.