Researchers are actively exploring cost-effective alternatives to traditional catalysts. For instance, the development of metal-organic frameworks (MOFs) and enzyme-based catalysts offers potential for reducing material costs. Additionally, advances in computational chemistry and machine learning are enabling the discovery of cheaper yet efficient catalyst materials through predictive modeling, potentially lowering R&D costs in the long run.
