期刊
JOURNAL OF MOLECULAR LIQUIDS
卷 360, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.molliq.2022.119513
关键词
Lentinan; Corrosion inhibitor; Electrochemistry; Surface characterization; Langmuir adsorption isotherm
资金
- National Natural Science Foun-dation of China [21672046, 21372054]
- Fundamental Research Funds for the Central Universities [HIT.NSRIF.201708]
- Research and innovation fund of Weihai Science and Tech-nology Development Plan Project
This study developed Lentinan (LNT) from mushroom Lentinus edodes as a corrosion inhibitor for Q235 steel in acid solution. The research showed that LNT exhibited good inhibition nature with high efficiency correlated to its concentration.
The developing effective and eco-friendly corrosion inhibitors plays a vital role in the protection of Q235 steel in acid solution. Accordingly, lentinan (LNT) from mushroom Lentinus edodes was developed here as a corrosion inhibitor for Q235 steel in 1M HCl. The inhibition performance was investigated by weight loss experiment, potentiodynamic polarization curve, electrochemical impedance test, scanning electron microscope and X-ray photoelectron spectroscopy. The research showed that LNT exhibited good inhibition nature for Q235 steel in acid medium, and the inhibition efficiency is correlated with LNT concentration. Potentiodynamic polarization curves stated clearly that its inhibition efficiency can be as high as 92.66% when the concentration is 100 mg/L on the corrosion of Q235 steel in 1M HCl. The change of impedance parameters showed that the corrosion inhibitor adsorbs on the surface of Q235 steel in the formation of a protective film. The adsorption behavior of polymer molecules follows Langmuir adsorption isotherm. SEM observation on the surface of Q235 steel showed that the metal matrix has a good anti-corrosion effect in the corrosion inhibitor solution. Molecular dynamics simulation was used to determine the relationship between the inhibitor and metal interactions.(c) 2022 Elsevier B.V. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据