Abstract This study employs multi-criteria decision-making (MCDM) methodologies to objectively rank nanomaterials for targeted wastewater treatment, specifically focusing on methylene blue (MB) dye and heavy metal (Pb(II), Cu(II), As(III)) removal. Using entropy-weighted Simple Additive Weighting (SAW) for MB removal and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) for heavy metals, nanomaterials were evaluated against key criteria: efficiency, adsorption capacity, and contact time. For MB removal, literature data of four calcium hydroxide (Ca(OH)₂) samples (S1–S4) were assessed across temperatures (25 °C, 35 °C, 45 °C), revealing S4 as optimal overall (performance index: 0.856). Temperature-specific ranks showed S2 dominant at 25 °C (index = 1.000) and 35 °C (marginally leading S4 by Δ ≈ 0.038), while S3 prevailed at 45 °C (Δ ≈ 0.012 over S4), with temperature critically influencing efficacy (R 2 rising from 0.0156 at 25 °C to 0.7726 at 45 °C). For heavy metals, poly(N-vinylcarbazole)–graphene oxide (GO) excelled in Pb(II) removal (similarity score: 0.677), CS/Romanian CPL dominated Cu(II) adsorption (score ≈ 0.980), and GO–ZrO(OH)₂ achieved ideal performance for As(III) (score = 1.000; 4 times more efficient than RGO–Fe(0)/Fe₃O₄). This MCDM framework enables data-driven selection of high-performance nanomaterials, optimizing wastewater treatment outcomes.
Zribi et al. (Mon,) studied this question.