Experimental and theoretical insights into Artemisia Stems aqueous extract as a sustainable and eco-friendly corrosion inhibitor for mild steel in 1 M HCl environment.
Asmae BerrissoulAli DafaliFouad BenhibaHalima OutadaIsmail WaradBurak DikiciAbdelkader ZarroukPublished in: Environmental science and pollution research international (2024)
The present research demonstrates an innovative investigation of environmentally friendly mild steel (M-steel) corrosion inhibition using the artemisia stems aqueous extract (ASAEx) as an inhibitor in hydrochloric acid 1 M. The standard extraction technique of hydrodistillation was used for producing the aqueous solutions of ASAEx. To assess the ratios of the chemical components, phytochemical screening was used to identify the stems of this plant. We used a variety of methods and techniques in our research on corrosion inhibition, including weight loss measures, surface analysis methods like XPS and SEM/EDS, electrochemical testing like PDP and EIS, as well as computational lead compound evaluation. Maximum inhibitory efficacy was achieved with 400 mg/L ASAEx in 1 M HCl at 303 K, i.e. 90%. The PDP investigation verified the mixed-kind inhibitor status of the ASAEx extract. To describe the surface of M-steel, fitting and synthetic data were used to identify a constant phase element (CPE). SEM surface analysis was also used to detect the ASAEx effect on the surface of M-steel. X-ray photoelectron spectroscopy (XPS) analysis shows the presence of trace molecules of ASAEx on M-steel surface characterizing the bands in Maj-ASAEx (major compound of ASAEx). Density functional theory (DFT) and molecular dynamics simulations (MDs) were used in computational chemistry to clarify the adsorption mechanism and inhibitory impact.
Keyphrases
- density functional theory
- molecular dynamics simulations
- weight loss
- oxidative stress
- high resolution
- ionic liquid
- anti inflammatory
- type diabetes
- gold nanoparticles
- computed tomography
- molecular docking
- magnetic resonance
- bariatric surgery
- big data
- artificial intelligence
- roux en y gastric bypass
- single molecule
- weight gain