Hinnant Katherine M, Sudol Paige E, Cramer Jeffrey A, Moore David W, Brown Loren C, Bunton Caleb M, Davis Matthew C, Snow Arthur W, Ananth Ramagopal
Chemistry Division, US Naval Research Laboratory, 4555 Overlook Ave SW, Washington, District of Columbia 20375, United States.
National Research Council Post-Doctoral Candidate Chemistry Division, US Naval Research Laboratory, 4555 Overlook Ave SW, Washington, District of Columbia 20375, United States.
ACS Omega. 2025 Sep 9;10(37):42270-42281. doi: 10.1021/acsomega.5c01726. eCollection 2025 Sep 23.
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 are reported. These values were compared to the calculated acute aquatic toxicity for 48 h exposure to 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.
开发了一个数据库，以促进表面活性剂结构、性质与泡沫灭火之间的关联，并开发机器学习模型。模型和结构-性质关系对于深入了解用于特定应用（如灭火）的表面活性剂设计和开发至关重要。该数据库包含71种单一表面活性剂和116种具有明确化学结构的混合配方的泡沫灭火指标。71种中的36种是合成的，其余为市售产品。此外，该数据库还包含13种市售单一表面活性剂和48种结构不明确的市售混合配方。性质包括表面活性剂临界胶束浓度、溶液表面张力、与燃料的界面张力、燃料引起的泡沫降解、庚烷通过泡沫层的通量以及在19厘米庚烷和汽油池火上的泡沫灭火性能。报告了7种表面活性剂和2种混合物在暴露48小时后的有限急性水生毒性测量值。使用两种方法（一种是生态结构活性关系，ECOSAR）将这些值与计算得出的暴露48小时的急性水生毒性进行了比较。这些程序无法解决表面活性剂多分散性问题，并且在对表面活性剂毒性进行分类时存在不一致性。两种表面活性剂混合物在急性水生毒性方面均表现出协同作用：Cap:G215比其单个组分毒性更大，G225:502W比其单个组分毒性更小。这些计算方法不应被用于评估该数据库中表面活性剂的潜在毒性，需要继续对表面活性剂混合物的急性水生毒性进行测试。该数据库以及持续的毒性测试有助于开发用于快速灭火、环境友好型消防泡沫的模型。
