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L-Glutathione reduced (GSH) is a small molecule peptide found in large quantities in living organisms, particularly in animal liver cells. Its unique structure gives it great potential to act as an environmentally friendly and efficient corrosion inhibitor. The inhibition properties of GSH for mild steel in simulated concrete pore solutions (SCPS) containing chlorine was investigated by electrochemical tests and SEM-EDS surface analysis. According to the experimental results, GSH mainly blocked the cathodic corrosion reaction and has a corrosion inhibition efficiency of up to 92.3% at room temperature, enhancing the corrosion resistance of mild steel. The isothermal adsorption results indicate that the adsorption behavior of GSH on the surface follows the Langmuir isothermal adsorption model. Both quantum chemical calculations (QC) and molecular dynamics simulations (MD) show that GSH can be effectively adsorbed onto mild steel substrates, thus protecting the surface from erosion. Finally, the results of the above experiments and theoretical calculations are combined to elucidate the inhibitive mechanism: GSH molecules binds to the iron surface via the sulfur, nitrogen and oxygen atoms in the molecule, thus adsorbing to the iron surface in a tiled manner and forming a protective film to prevent the corrosive material from contacting the iron surface. [ABSTRACT FROM AUTHOR]

To deal with set-valued data, one usually exploits the information value similarity, which is fed back to the object set. Alternatively, it is feasible but scarcely considered to handle set-valued data from the perspective of the information-value similarity which is fed back to the feature set. Based on this perspective, this paper studies feature selection for a set-valued decision information system (SVDIS) by means of fuzzy rough iterative computation model. In order to describe the similarity between objects, the fuzzy symmetric relations in an SVDIS are first defined, and a variable parameter to control the similarity is introduced. Then, new fuzzy rough set model for set valued data is proposed, and this model employs the iterative computation strategy to define fuzzy rough approximations and dependency functions. Next, fuzzy rough iterative computation model is established by using the iteration of fuzzy positive region and fuzzy dependency. Moreover, fuzzy rough iterative computation model is applied to the feature selection for set-valued data, and the corresponding algorithm is given. Finally, experiments are carried out to evaluate the performance of the given algorithm. The experimental results indicate that the given algorithm is more effective than some existing algorithms. [ABSTRACT FROM AUTHOR]