Green Synthesis and Process Optimization of Zinc Oxide Nanoparticles Using Pseudomonas fluorescens: Taguchi–ANN-Based Evaluation of Physico-Chemical Parameters

Year 2026, Volume: 10 Issue: 1, 204 - 212, 16.12.2025

Thanikodi, Sathish , Jothi Arunachalam Solairaju

https://doi.org/10.31127/tuje.1804146

Abstract

The green production of metal nanoparticles is attracting interest due to its environmentally benign manufacturing methods. Zinc oxide (ZnO) nanoparticles possess significant uses as semiconductors, antimicrobials, and photothermal materials; nevertheless, their production by microbes is still inadequately investigated. This study involved the synthesis of ZnO nanoparticles utilizing Pseudomonas fluorescens, with optimization of the physico-chemical incubation parameters affecting nanoparticle size and polydispersity index (PDI). The biogenic synthesis was validated using ATR-FTIR and X-ray diffraction analysis. Taguchi's L27 orthogonal array and ANOVA at a 95% confidence level were utilized to ascertain the most relevant components. The analysis indicated that pH and aeration were the predominant characteristics, whereas temperature and concentration exhibited modest impacts, and duration had negligible influence. The ideal parameter combination was identified as pH of 7, temperature 40 °C, aeration 220 rotation per minute, duration 3 days, and zinc acetate of 2 mm, resulting in the minimal particle size and lowest PDI. SEM results corroborated the appearance and dimensions anticipated by the model. The findings emphasize that regulating pH and aeration is essential for improving the biogenic synthesis of ZnO nanoparticles, whilst other parameters operate as secondary tuning variables. The ANN model optimized the expected values of particle size at 157.69 nm and PDI at 0.601 for the chosen process conditions, demonstrating substantial concordance with experimental results of 208.54 nm and 0.76, respectively. The research was utilized to enhance the process parameters in industrial systems for sustainable application

Keywords

Biogenic Synthesis , Zinc Oxide Nanoparticles , Taguchi Optimization , Artificial Neural Network , Modeling

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