A dataset for aqueous surfactant phase behavior as a function of temperature and composition

Area of Science:

• Physical Chemistry
• Materials Science
• Chemical Engineering

Background:

• Understanding surfactant phase behavior is crucial for formulation development.
• Existing data on surfactant phase diagrams can be limited or difficult to access.
• Digitizing and standardizing phase behavior data can accelerate research and application.

Purpose of the Study:

• To present a comprehensive dataset (PhDat) of surfactant aqueous phase behavior.
• To classify discretized state points into distinct phase states, including single- and two-phase regions.
• To provide a framework for probabilistic phase transition analysis and handling of biphasic gaps.

Main Methods:

• Discretization of aqueous phase behavior for 143 surfactants across varying temperature and composition.
• Classification of state points into 118 distinct phase states.
• Development of a workflow for obtaining digitized phase diagrams.

Main Results:

• A dataset encompassing 143 surfactants with detailed phase behavior information.
• Probabilistic classification of phase transitions and narrow biphasic gaps.
• A defined workflow for generating digitized phase diagrams.

Conclusions:

• The PhDat dataset offers a valuable resource for surfactant formulation research.
• The dataset is suitable for machine learning applications in predicting and optimizing surfactant behavior.
• The extensible design allows for future incorporation of diverse surfactant mixtures and non-surfactant systems.