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

Area of Science:

• Colloid and Surface Science
• Materials Science
• Chemical Engineering

Background:

• Oil-in-water nanoemulsions (NEs) are crucial for delivering hydrophobic compounds across diverse industries like food, cosmetics, and pharmaceuticals.
• High-energy ultrasonication (HEU) is a common method for NE preparation, but parameter effects on formation and properties are not fully understood.
• A knowledge gap exists regarding how experimental parameters and component characteristics influence NEs produced via HEU.

Purpose of the Study:

• To comprehensively screen factors influencing NE formation and quality using HEU.
• To apply artificial neural networks (ANN) for determining the relevance of each parameter.
• To establish predictive models for rational NE design.

Main Methods:

• Systematic screening of factors including oil viscosity, density, surfactant type, processing parameters, and formulation composition.
• Utilizing artificial neural networks (ANN) to analyze parameter relevance and predict NE properties.
• Investigating the impact of processing temperature, ultrasound amplitude, and pulse mode on NE characteristics.

Main Results:

• Oil viscosity emerged as the dominant factor influencing droplet size (Zavg) and polydispersity index (PDI).
• Higher processing temperatures and specific ultrasound settings (amplitude, pulse mode) improved NE formation, especially for high-viscosity oils.
• ANN models accurately predicted NE properties and identified critical viscosity thresholds (<300 nm Zavg, <0.4 PDI) for successful nanosized emulsification.

Conclusions:

• Oil viscosity is the primary determinant for successful NE formation via HEU.
• Processing parameters and temperature significantly impact NE quality, particularly under challenging conditions.
• The study provides a predictive framework for optimizing NE design and formulation using HEU.