Advanced reliability and parametric influences on the electrochemical-based inhibition efficiencies of diaminohexane and diaminooctane grafted graphene oxide

The challenge of corrosion in industrial applications necessitates effective inhibition strategies to protect materials like X60 carbon steel from corrosive environments. This study presents a reliability-based analysis and parametric influence assessment of corrosion inhibition efficiencies for diaminohexane (DAH) and diaminooctane (DAO) grafted graphene oxide (GO), using previously published electrochemical data on X60 carbon steel in 15 % HCl solution. Rather than generating new experimental results, the focus is on extracting deeper insights from existing data obtained through electrochemical impedance spectroscopy (EIS), linear polarization resistance (LPR), and potentiodynamic polarization (PDP). To validate data consistency, statistical stationarity tests; Augmented Dickey-Fuller (ADF) and Phillips-Perron (PP) were applied, for instance, the ADF test for concentration (Concn) yielded a test value of −3.3253 (p-value = 0.0395) after the first differencing, indicating stationarity. Similarly, the PP test for corrosion inhibition efficiency (Icorr) had a test value of −47.4373 (p-value = 0.0001), confirming data reliability. Dependency analysis further enhanced model transparency by recognizing the impact of individual properties on the predictions confirming the reliability of the dataset. Furthermore, SHAP dependency analysis was employed to assess the impact of key experimental parameters on inhibition efficiency, enhancing model interpretability. Despite the limitations of small datasets, this study shows that high-quality, well-controlled data can yield reliable insights and support robust predictive modeling in corrosion science. The approach lays the foundation for future studies on long-term inhibitor performance, environmental impact, and the integration of data-driven methods in corrosion management.