Simplex-lattice design and decision tree optimization of endophytic Trichoderma-multi-walled carbon nanotube composite for enhanced methylene blue removal - Sahar E. Abo-Neima; Emad M. Elsehly; Fatimah O. Al-Otibi; Mohammed M. El-Metwally; Yosra A. Helmy; Noha M. Eldadamony; WesamEldin I.A. Saber; Adel A. El-Morsi (Heliyon)
This study investigates a novel approach for enhancing methylene blue (MB) removal from water using a composite of endophytic Trichoderma mate and multi-walled carbon nanotubes (MWCNTs). For the first time, a unique combination of simplex-lattice design and decision tree learning algorithm was employed to optimize MB removal. This innovative approach effectively identified the optimal composite ratio of hyphal mate (0.5354 g/L) and MWCNTs (0.4646 g/L) for maximizing MB removal, which achieved remarkable removal efficiency ranging from 63.50 to 95.78 % depending on the combination used. The DT model further demonstrated promising potential for predicting MB removal efficiency. SEM revealed a unique hybrid material formed by the intertwining or entrapment of MWCNTs within the hyphal network of Trichoderma mate. FT-IR analysis confirmed the presence of novel functional groups on the MWCNTs' surface at 2438.79 and 528.25 cm−1, likely due to interactions with the endophytic fungi's biomolecules. These functional groups presumably act as reducing and stabilizing agents, promoting efficient MB adsorption. This research paves the way for utilizing the combined biological and chemical approach (fungal biomass and MWCNTs) in bioremediation applications. The findings suggest significant potential for practical applications in wastewater treatment, providing an eco-friendly and cost-effective method for dye removal. Furthermore, the proposed method shows promise for scaling up to industrial wastewater treatment and applicability in resource-limited settings, offering a sustainable solution for global water pollution challenges. Further investigations with larger datasets incorporating additional influencing factors are necessary to refine the predictive power of the DT model for practical applications.