The Effect of Impurities in Nickel Grain Boundary: Density Functional Theory Study

By means of density functional theory, we investigate the effect of impurities on structural, electronic and mechanical properties on Nickel Σ5 grain boundary (GB) and its free surface, by studying the effect of 11 transition metal impurities and 8 light elements. The calculation of segregation energy, cohesive energy, formation energy, GB embrittling potency and theoretical tensile strength combined helps us to give accurate conclusions about the effect of these impurities and to compare them with the available experimental and theoretical results. We used the obtained results that are on “equal footing” to establish some correlations and trends. We also confirmed that sulfur and oxygen are the most embrittling elements in Nickel GB in accordance with established literature results and that transition metal elements have a general tendency to segregate to the grain boundaries in a moderate way. Unlike the studied light elements, these elements tend to strengthen the Ni grain boundaries, especially W and Te

Insights on the effect of water content in carburizing gas mixtures on the metal dusting corrosion of iron

Constituents of syngas, such as water, carbon monoxide and sulfides, can cause the degradation of the steel pipes they move through, leading to carbon dusting and corrosion. In spite of considerable attention to this process, many questions remain about its origin. We conduct reactive molecular dynamics simulations of multi-grain iron systems exposed to carburizing gas mixtures to investigate the effect of water content on metal dusting corrosion. To simulate carbon monoxide (CO) dissociation followed by carbon diffusion, we employ an extended-ReaxFF potential that allows accounting for both the high C atoms coordination in bulk iron as well as the lower C coordination at the iron surface and interfaces. The reactions happening in the sample at different water con- centrations and at different time frames are explored. We demonstrate that the presence of water on a clean Fe surface promotes different catalytic reactions at the beginning of the simulations that boost the C, H, O diffusion into the sample. At later stage, the formation of oxide scale leads to an elevated concentration of H2O/OH molecules on the surface due to the decrease in Fe affinity to dissociate water. This results into blocking the Fe catalytic sites leading to lower C and O diffusion to the bulk of the sample

Accelerating the Design of Photocatalytic Surfaces for Antimicrobial Application: Machine Learning Based on a Sparse Dataset

Nowadays, most experiments to synthesize and test photocatalytic antimicrobial materials are based on trial and error. More often than not, the mechanism of action of the antimicrobial activity is unknown for a large spectrum of microorganisms. Here, we propose a scheme to speed up the design and optimization of photocatalytic antimicrobial surfaces tailored to give a balanced production of reactive oxygen species (ROS) upon illumination. Using an experiment-to-machine-learning scheme applied to a limited experimental dataset, we built a model that can predict the photocatalytic activity of materials for antimicrobial applications over a wide range of material compositions. This machine-learning-assisted strategy offers the opportunity to reduce the cost, labor, time, and precursors consumed during experiments that are based on trial and error. Our strategy may significantly accelerate the large-scale deployment of photocatalysts as a promising route to mitigate fomite transmission of pathogens (bacteria, viruses, fungi) in hospital settings and public places

Elucidating the role of extended surface defects at Fe surfaces on CO adsorption and dissociation

The adsorption and dissociation of hydrocarbons on metallic surfaces during catalytic reactions in a steam reforming furnace often lead to the carburization of the catalysts and metallic surfaces involved. This process is greatly accelerated by the presence of intrinsic defects like vacancies and grain boundaries and is succeeded by surface to subsurface diffusion of C. We employ both density functional theory and reactive force field molecular dynamics simulations to investigate the effect of surface defects on CO dissociation rate directly related to metaldusting corrosion. We demonstrate that stable surface vacancy clusters with large binding energies accelerate the adsorption of CO molecules by decreasing the corresponding dissociation energies. In addition, we demonstrate that the appearance of multiple GBs at the surface leads to an enhancement of the CO dissociation rate. Furthermore, we demonstrate that the increase in surface roughness by emerging GBs leads to an increase in CO dissociation rate