An Artificial Neural Network-based Model for Antibacterial Activity of CuO/Al₂O₃/Chitosan Nanocomposites
J. Environ. Nanotechnol., Volume 15, No 2 (June 2026) pp. 354-369
Abstract
The increasing contamination of aquatic environments by industrial effluents, agricultural runoff, and domestic waste has created a critical demand for advanced antibacterial materials with reliable predictive capabilities. A ternary CuO/Al₂O₃/chitosan (CS) nanocomposite was developed to address this challenge, where CuO provides photocatalytic activity, Al₂O₃ enhances dispersion and structural stability, and chitosan contributes as a biopolymeric antibacterial matrix. The nanocomposite beads were synthesized via an ex situ mechanical mixing followed by ionic gelation using NaOH, and their antibacterial performance was evaluated against Escherichia coli and Pseudomonas aeruginosa using the disc diffusion method. An Artificial Neural Network integrated with Bayesian Optimization (ANN–BO) was employed to model and optimize the nonlinear relationship between material composition and antibacterial activity. The results demonstrated inhibition zones ranging from 1.8 to 6.1 mm for E. coli and 3.0 to 10.0 mm for P. aeruginosa with predicted values closely matching experimental outcomes (residuals within −1.80 to +0.93 mm and −1.01 to +1.79 mm). The optimized CuO/Al₂O₃/CS (2:2:1) nanocomposite exhibited the highest antibacterial activity with a maximum inhibition zone of 10.0 mm against P. aeruginosa, while the 2:1:1 composition showed optimal performance against E. coli with an inhibition zone of 6.1 mm. The ANN–BO model exhibited high predictive accuracy (R ≈ 0.935). The developed framework demonstrates strong potential for the design of efficient antibacterial nanocomposites for water treatment and antimicrobial coating applications.
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Reference
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