Hydrogels, three-dimensional polymeric networks capable of absorbing large amounts of water, have garnered significant attention for their potential applications in diverse fields, including tissue engineering, drug delivery, soft robotics, and artificial joints [1, 2, 5]. However, achieving a combination of high mechanical strength, toughness, and low friction, particularly under load-bearing conditions, remains a significant challenge for conventional single-network hydrogels. Triple network (TN) hydrogels, which combine three interpenetrating polymer networks with different properties, have emerged as a promising strategy to overcome these limitations by incorporating multiple energy dissipation mechanisms and synergistic interactions between the networks [35, 42, 43]. This article reviews the design strategies and underlying synergistic enhancement mechanisms responsible for the robust mechanical properties and low friction of triple network hydrogels. We discuss the role of different network components (e.g., brittle, ductile, and reinforcing networks), crosslinking methods (chemical and physical), and the interplay between network structure and macroscopic properties. The analysis highlights how the synergistic effects of multiple networks contribute to enhanced toughness, fatigue resistance, and lubrication, making TN hydrogels promising candidates for applications requiring high mechanical performance and low friction, such as artificial cartilage.

This study critically evaluates the fidelity of published correlation parameters used in modeling liquid-liquid equilibrium (LLE) systems. As accurate phase behavior predictions are vital for designing separation processes, particularly in solvent extraction and chemical engineering applications, the reliability of reported parameters—often derived from empirical or semi-empirical models—remains under scrutiny. Through a comprehensive review and re-evaluation of selected literature datasets, this research identifies discrepancies between experimental data and model predictions, highlighting instances of poor parameter reproduction, inconsistencies in data sources, and gaps in methodological transparency. The analysis underscores the importance of standardization, reproducibility, and rigorous validation in publishing LLE parameters. Ultimately, the findings advocate for enhanced peer-review protocols and the adoption of open-access databases to improve trust and utility in LLE modeling across academia and industry.