The effects of cold work on the pitting corrosion resistance of a nickel-free high-nitrogen stainless steel in chloride solution have been investigated by electrochemical tests, surface chemical analysis, immersion tests, and microscopic observations. Potentiodynamic polarization revealed that pitting resistance was degraded by cold work as convinced by the decreased critical pitting potential. This could be due to a less compact and protective anodic passive film based on the results of electrochemical impedance spectroscopy, Mott–Schottky measurement, and X-ray photoelectron spectroscopy analysis. The growth of such an imperfect passive film could be attributed to a high density of deformation bands and other defects introduced by cold work. Scanning electron microscopy observation of the pitted specimens after polarization tests showed no obvious change in size and density of corrosion pits with increasing cold work level, whereas immersion tests showed varied pit density with cold work although the average size of pits did not increase linearly as a function of cold work level. The effects of cold work on the characteristics of passive films are discussed.

In this work, we demonstrate the feasibility of a computational approach based on first principles for estimating various thermomechanical quantities of a cross-linked epoxy resin. In particular, this work is focused on determining estimated values of the variation in glass transition temperature, coefficient of thermal expansion, volume shrinkage due to curing, Young’s modulus, Poisson’s ratio, yield strength, and viscosity as a function of temperature and degree of curing via molecular dynamics simulations. In most cases it has been demonstrated that the values predicted by the proposed approach are in good agreement with the respective experimentally measured values. In addition, the validity of the proposed models describing the dependence of the thermomechanical quantities on temperature and curing degree is examined. Throughout this study, we demonstrate that the molecular dynamics–based computational predictive framework can serve as an excellent infrastructure that can enable numerical prediction of materials properties and thereby can reduce the costs of associated with physical experimentation. In addition, we demonstrate that insightful information can be generated at the molecular and microscopic scales that is not easily extractable from experiments.