Evaluation of the corrosion inhibition potential of Azadirachta indica leaves on thermo mechanically treated steel rebars using an artificial neural network, electrochemical and spectroscopic approach

Abstract

The corrosion inhibition performance of Azadirachta indica leaves as a green corrosion inhibitor on Thermo Mechanically Treated (TMT) steel exposed to 1 M HCl and 1 M NaCl was investigated using weight loss, electrochemical, spectroscopic and surface analysis techniques. The results depicted that neem leaf powder was effective in inhibiting corrosion in TMT rebars. The findings from weight loss showed that, maximum inhibition efficiency of 93.264% (acidic) and 85.937% (salt) was attained for the highest concentration of inhibitor and using Artificial Neural Network, the corrosion rate was predicted, and the performance was assessed via statistical parameters to obtain an excellent fit model to data. The corrosion rate and polarisation resistance of steel at 13.628 mm/year, 24.8 Ω (acidic) and 0.27396 mm/ year, 366 Ω (salt) was obtained and the type of inhibitory mechanism was found via Tafel and Electrochemical Impedance spectroscopic analysis for the optimum concentration of inhibitor. The phytochemical compounds, heteroatoms and functional groups adsorbed on the surface of steel to hinder corrosion were identified through Gas-Chromatography Mass-Spectroscopy and Fourier-Transform Infrared Spectroscopic techniques. Samples admixed with neem powder showed less visible cracks, pits and surface roughness formation as seen through Field Emission Scanning Electron Microscopy and Atomic Force Microscopy.

Keywords

• Green corrosion inhibitor
• TMT
• EIS
• artificial neural network
• polarization
• Civil, Environmental and Geotechnical Engineering