Sustainable hybrid rubber nanocomposites: a review of green solvents processing and machine learning-driven optimization

The rubber industry faces increasing pressure to transition from energy-intensive, fossil-fuel-based processing to sustainable manufacturing. This review critically assesses the development of green rubber hybrid nanocomposites, focusing on the synergistic integration of green solvents-specifically Ionic Liquids (ILs) and Deep Eutectic Solvents (DESs)-with Artificial Intelligence (AI) and Machine Learning (ML) optimization. We first evaluate the efficacy of ILs and DESs as sustainable alternatives to volatile organic compounds (VOCs) for enhancing hybrid nanofiller dispersion (e.g., silica and carbon black) and interfacial compatibility. A comparative analysis highlights that while ILs offer superior thermal stability and tunability, DESs present a more viable pathway for industrial scale-up due to their biodegradability, significantly lower cost, and ease of synthesis. Secondly, to address the high dimensionality and time constraints of optimizing these novel multi-component hybrid formulations, we review recent ML-driven approaches. We demonstrate how data-driven algorithms, including Artificial Neural Networks (ANN) and Gaussian Process Regression (GPR), accelerate material discovery by accurately predicting key rheological and mechanical properties, thereby clarifying complex structure-property relationships and reducing the reliance on traditional trial-and-error experimentation. Herein, we conclude by identifying a critical research opportunity: the convergence of green solvent chemistry with computational intelligence offers a robust framework for designing the next generation of high-performance, environmentally sustainable elastomers.