Synthesis and physicochemical characterization of ZnO-modified Fe₂O₃ nanoparticles with HCNFF-based prediction and AQAI-Net-based optimization for environmental applications

Abstract Nanotechnology has paved the way for the development of Fe₂O₃ nanoparticles modified by ZnO showing high adsorption capacity and enhanced catalytic activity, which have gained great interest for advanced environmental remediation and photocatalytic treatment of wastewater. The use of intelligent computational tools in the synthesis of nanomaterials further extends pollutant degradation abilities, structural strength and environmental adaptability in varying operational conditions. However, prediction and optimization methods are existing and have poor prediction accuracy, high computational complexity, low convergence stability and low environmental adaptability. To overcome the above constraints, this research presented a Hierarchical Cognitive Neuro Forecasting Framework (HCNFF) and Adaptive Quantum Automation Intelligence Network (AQAI-Net) to realize intelligent prediction of nanoparticles and automatic optimization of the environment. The HCNFF predicted the photocatalytic degradation efficiency and adsorption performance with the hierarchical feature reconstruction and adaptive neural forecasting and cognitive learning mechanisms. Simultaneously, AQAI-Net implemented quantum-inspired optimization, adaptive learning and automatic control regulation for optimization of synthesis parameters. Experimental results showed that the system performance was better with a maximum prediction accuracy of 98.88% and optimization accuracy of 98.91% respectively. The general proposed framework was found to be very useful to propose a scalable and reliable solution for the sustainable environmental nanotechnology applications.