Combined experimental, theoretical, & machine learning studies of anticorrosion properties of hydroxyethyl cellulose, l-glutamic acid, & potassium acesulfame-derived carbon dots

Moses M. Solomon, Ubani O. Amune, Xiaomeng He, Hairun Wang, Jun He, Di Hu, Fiseha Berhanu Tesema, Hainam Do, Abdelkarim Ait Mansour, Rachid Salghi, Saviour A. Umoren

Research output: Journal PublicationArticlepeer-review

Abstract

This study investigates the corrosion inhibition performance of l-Glutamic acid (GA), potassium acesulfame (AK), hydroxyethyl cellulose (HEC), and novel N, S co-doped carbon dots (N, S-CDs) on carbon steel (CS) and stainless steel (SS) in 1 M HCl. While GA and AK individually inhibited CS (86–87 % efficiency (η) via adsorption) but accelerated SS corrosion (−66 % to −70 %) due to chelation-driven dissolution and cathodic activation, their combination with KI reversed SS corrosion acceleration to inhibition (42–44 %) through synergistic co-adsorption. To address this metallurgical specificity, N, S-CDs were synthesized from HEC, GA, and AK, demonstrating exceptional dual inhibition (90 % for CS, 66 % for SS at 0.5–1.0 mg/L) via chemisorption, which forms protective films, as validated by TEM/AFM and XPS. X-ray photoelectron spectroscopy (XPS) and computational modeling (DFT, MD simulations) reveal N, S-CDs’ planar adsorption, Fe–N/S bonding, and electron donation as key mechanisms. Machine learning identified inhibitor concentration as the dominant predictor of efficiency, highlighting the importance of surface coverage dynamics. The N, S-CDs exhibit thermal resilience (97 % η for CS at 60 °C by 1.0 mg/L) and prolonged stability (98 % for CS at 72 h by 0.5 mg/L), outperforming conventional inhibitors. This work introduces N, S-CDs as a novel, eco-friendly solution for multi-metallurgical corrosion protection, bridging the gap between organic inhibitors’ limitations and the demand for adaptive, high-performance materials in aggressive environments.
Original languageEnglish
Article number120772
JournalCarbon
Volume245
DOIs
Publication statusPublished - Oct 2025

Keywords

  • Corrosion
  • Carbon dots
  • Multi-protection
  • Machine learning
  • prediction
  • Eco-friendliness

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