Corrosion inhibition pre-screening of Nitrobenzaldehyde Meldrum’s acid using response surface methodology (RSM) and Pearson correlation analysis

Abstract Corrosion inhibitors are chemical agents that, even at low concentration, markedly mitigate the rate of metal degradation in acidic media. The objective of this study is to screen and evaluate the corrosion inhibition of Nitrobenzaldehyde Meldrum’s Acid (NitroMA) in HCl medium. The NitroMA compound was successfully synthesized through a condensation method and characterized using ATR-FTIR, NMR, and elemental analysis. Weight loss measures revealed that the corrosion inhibition efficiency of NitroMA increases with its concentration in 1 M HCl concentration. The inhibition efficiency of NitroMA was as high as 80.94 % at 5 mM in 1 M HCl. The alignment with Langmuir and Frumkin models indicates that inhibition efficiency arises from a combination of monolayer adsorption and intermolecular attraction across different surface regions, which may lead to more stable and compact monolayers on the metal surface as confirmed by the SEM-EDX analysis. Both Langmuir and Frumkin isotherms fitted the data with high correlation coefficients (R 2 > 0.999). The Langmuir model yielded a Gibbs free energy of adsorption around (−21.1 kJ mol −1 ), suggesting a borderline case between physisorption and chemisorption. In contrast, the Frumkin model gave a lower value (−9.11 kJ mol −1 ), consistent with physisorption. The variation arises because Langmuir assumes a homogeneous surface with no adsorbate–adsorbate interaction, while Frumkin accounts for lateral interactions. Therefore, the compound primarily undergoes physisorption, but the Langmuir result also indicates possible contribution of weak chemisorption. Following this, NitroMA was screened using Response Surface Methodology (RSM) with two-factors analysis, incorporating temperature, acid concentration, inhibitor concentration, time, and ratio of volume acid and inhibitor as the parameters. The developed RSM model demonstrated a good fit, as reflected by the statistical parameters: R 2 = 0.8962, Adjusted R 2 = 0.8851, and Predicted R 2 = 0.8644, indicating high reliability and predictive accuracy. The results showed that temperature, time, and acid concentration significantly influence the inhibition efficiency. These findings were validated by computational analysis using the Pearson correlation analysis, which demonstrated that temperature, acid concentration, and time are the most strongly linked to parameters with inhibition efficiency.