Unveiling the Role of Formulation and Process Variables in Nanoemulsion Preparation: A Data-Driven Approach Using...

Background: Oil-in-water nanoemulsions (NEs) represent versatile platforms for the delivery of hydrophobic compounds and find a wide range of applications in different fields such as food, cosmetics, agriculture, pharmaceutics, and oil and gas industries. Various methodologies can be applied for the preparation of NEs as low-energy and high-energy methods. Among them, high-energy ultrasonication (HEU) is a popular technique in research laboratories or small manufacturing facilities. However, a clear gap remains in understanding how, and to what extent, experimental parameters and the chemical and physical characteristics of the components affect the formation and properties of NEs through HEU. Methods: In this work, a comprehensive screening of factors (oil viscosity and density, surfactant type, processing parameters, and formulation composition) affecting NEs formation and quality was performed and an artificial neural network (ANN) was applied to determine the relative relevance of each parameter. Results: Oil viscosity revealed to be the primary factor affecting droplet size (Z_avg) and polydispersity index (PDI), with high-viscosity oils leading to poor emulsification into nanosized droplets. Higher processing temperatures improved NE formation by reducing viscosity during sonication. Ultrasound amplitude and pulse mode influenced NE characteristics, particularly under challenging conditions. Surfactant type and oil content had, instead, minor effects on the NEs' features. ANN modelling accurately predicted NEs' properties and identified critical viscosity limits for successful nanosized emulsification (Z_avg < 300 nm and PDI < 0.4). Conclusions: These findings provide a predictive basis for rational NE design under HEU, serving as a guide for researchers working in different fields.