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The growing global demand for safe and sustainable freshwater production has fueled interest in solar distillation systems. Although solar stills offer environmentally friendly desalination solutions, their low productivity remains a major problem. The incorporation of nanofluids based on oxide nanoparticles has emerged as a promising approach to enhance the thermophysical performance and freshwater yield of solar stills. However, selecting the most suitable nanoparticle is challenging due to conflicting thermophysical, environmental, and economic criteria. To address this decision-making complexity, this study proposes a novel hybrid multi-criteria decision-making (MCDM) framework that integrates entropy and centroidous objective weighting methods with the “Multi-Attributive Ideal-Real Comparative Analysis” (MAIRCA) ranking technique. Twelve widely reported oxide nanoparticles (SiO 2 , Fe 3 O 4 , MgO, ZnO, CuO, Al 2 O 3 , GO, TiO 2 , Co 3 O 4 , CeO 2 , SnO 2 and ZrO 2 ) were evaluated against eight criteria: density, thermal conductivity enhancement, specific heat capacity, thermal expansion coefficient, cost, toxicity, stability, and compatibility with the container material. Entropy-centroidous weighting identified thermal conductivity (0.2962) and cost (0.1969) as the most influential criteria, while MAIRCA ranked GO first with a score of 0.0357, followed by Al 2 O 3 (0.0363) and SiO 2 (0.0411); ZnO ranked last with a score of 0.0582. Comparative validation across eleven established MCDM methods showed strong agreement, with Spearman correlation coefficients above 0.748, p-values below 0.05, while mean absolute error values not exceeding 1.83. Sensitivity analysis further confirmed that GO remained at the top position in almost all scenarios, except when the importance of thermal conductivity started to decrease compared to its actual weight, resulting in its replacement by Al 2 O 3 . The proposed framework provides a systematic and transparent decision-support tool for nanoparticle pre-screening in solar still applications. The entropy-centroidous-MAIRCA framework can be extended to a wide range of problems related to renewable energy and thermal management optimization.
Negi et al. (Tue,) studied this question.