Biosensors and Nanotheranostics
Bionanotechnology, Drug Delivery, Therapeutics | online ISSN 3064-7789
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RESEARCH ARTICLE   (Open Access)
Contents Vol 3 (1)

Green Synthesis and Zinc-Oxide Nanoparticles for Corrosion Inhibition and Modeling Corrosion Inhibition of Mild Steel in HCl Solutions

Abuchi Elebo 1*, Uba Sani 1, Patricia A. Ekwumemgbo 1, Victor O. Ajibola 1

+ Author Affiliations

Biosensors and Nanotheranostics 3 (1) 1-17 https://doi.org/10.25163/biosensors.317339

Submitted: 20 January 2024 Revised: 28 March 2024  Published: 30 March 2024 

Abstract

Background: The expanding field of nanotechnology has led to significant interest in green chemistry approaches for synthesizing metal nanoparticles, which offer environmentally friendly, cost-effective, and recyclable solutions. Among these, zinc-oxide nanoparticles (ZnONPs) have gained attention for their potential as corrosion inhibitors. This study introduces a novel green synthesis of ZnONPs using Opuntia fragilis leaves (OFL) and explores their effectiveness in inhibiting the corrosion of mild steel in hydrochloric acid (HCl) solutions. Methods: ZnONPs were biosynthesized using OFL, and their physicochemical properties were characterized through SEM, TEM, XRD, EDX, UV-Vis, and FT-IR spectroscopy. The nanoparticles' average size, shape, elemental composition, and crystallinity were determined. The corrosion inhibition potential of OFL-ZnONPs on mild steel in various HCl concentrations was evaluated using weight loss measurements, electrochemical impedance spectroscopy, and potentiodynamic polarization. Numerical optimization was performed using response surface methodology (RSM) and artificial neural network (ANN) models to predict and optimize the process parameters influencing corrosion inhibition. Results: The characterization revealed ZnONPs with an average internal size of 15 nm, external size of 25 nm, hexagonal shape, and a crystallinity of 68.14%. Elemental analysis showed high zinc and oxygen content (75.23% and 23.45%, respectively). Corrosion inhibition studies indicated that as the inhibitor concentration increased, weight loss decreased, resulting in higher inhibition efficiency. RSM optimization yielded a maximum inhibition efficiency of 77.31% under specific conditions (2 M HCl, 4.75 hours, 0.4 g/L inhibitor concentration, and 324.5 K). ANN optimization identified the optimal neuron number as 9, with a mean squared error (MSE) of 0.6053, confirming the robustness of the model. Conclusion: This study demonstrates the successful green synthesis of ZnONPs using OFL and their effective application as corrosion inhibitors for mild steel in HCl solutions. The use of RSM and ANN for process optimization highlighted the robustness and predictive accuracy of these models, paving the way for environmentally friendly and efficient corrosion inhibition strategies in industrial applications.

Keywords: Green synthesis, zinc oxide nanoparticles, corrosion inhibition, mild steel, response surface methodology, Opuntia fragalis

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