Sustainable Elastomer Development Using Computational Intelligence and Bio-Based Modification Techniques

The transition toward environmentally responsible materials has intensified research on sustainable elastomers that combine high performance with reduced ecological impact. This study presents an integrated framework for elastomer development that merges computational intelligence with bio-based modification strategies. Machine learning algorithms were employed to model structure-property relationships and predict optimal formulations based on mechanical strength, elasticity, thermal stability, and biodegradability targets. Experimental validation was conducted using partially bio-derived polymers, natural fillers, and green crosslinking agents to replace conventional petroleum-based additives. Bio-based modifiers, including plant-derived plasticizers and lignocellulosic reinforcements, were incorporated to enhance flexibility and tensile performance while minimizing carbon footprint. Computational optimization significantly reduced trial-and-error experimentation by identifying synergistic interactions among polymer matrices, curing systems, and bio-additives. The resulting elastomer composites demonstrated improved sustainability metrics without compromising durability or processability. Life-cycle considerations indicated measurable reductions in energy consumption and greenhouse gas emissions compared with traditional synthetic elastomers. The findings highlight the potential of combining data-driven design with renewable material engineering to accelerate the development of next-generation elastomers suitable for automotive, medical, and industrial applications. This approach establishes a scalable pathway toward circular material innovation and responsible manufacturing practices.