Zwitterionic Materials for Water Treatment and Food Packaging: Synthesis, Performance, and Machine Learning-Driven Design

The growing demand for high-performance materials in water treatment and food packaging has intensified research into zwitterionic-based structures due to their exceptional hydrophilicity, fouling resistance, and functional versatility. This review presents a comprehensive analysis of recent advances in the synthesis, characterization, and performance evaluation of zwitterionic materials for these critical applications, with particular emphasis on emerging machine learning approaches that enable their rational design and optimization. By summarizing key findings and identifying current challenges, this work aims to bridge the gap between laboratory-scale innovations and real-world implementation. Among various synthesis strategies, polymerization has emerged as the most effective method for incorporating zwitterionic functionalities. Morphological studies further demonstrate that zwitterionic modification significantly enhances both adsorptive and barrier properties. Machine learning models have shown strong potential in accelerating the development of zwitterionic materials by establishing structure–property relationships that predict and optimize antifouling performance. By integrating insights from multiple disciplines, this review outlines a strategic framework for transforming fundamental research into practical solutions that promote environmental sustainability and food security.