| Abstract Scope |
Magnetic refrigeration (MR) is an energy-efficient and environmentally sustainable alternative to conventional cooling technologies. Ferromagnetic perovskite manganites (R₁₋ₓTₓMnO₃, where R is a rare-earth element and T a transition metal) are promising magnetocaloric materials due to their tunable Curie temperatures (100–350 K), low density, and scalable synthesis. This study systematically evaluates and ranks hundreds of such compositions using an integrated framework combining multi-attribute decision-making (MADM) and advanced statistical (AS) methods. Key performance metrics include magnetic entropy change, refrigerant capacity, and Curie temperature. MADM approaches—TOPSIS, Grey Relational Analysis (GRA), and Operational Competitive Ratio (OCR)—yield consistent rankings across compositions. Statistical analyses further validate these results and cluster materials with comparable performance characteristics. The combined methodology identifies top-performing candidates suitable for both large- and small-scale MR applications. This work provides a robust, data-driven pathway for screening and optimizing magnetocaloric materials, supporting the advancement of next-generation, sustainable cooling technologies. |