Two-phase model for inverse Hall–Petch effect in nanocrystalline thin film: Atomistic simulation study
Source abstract
Abstract
This work presents two methods to improve the understanding of the mechanical behavior of nanocrystalline thin films. Firstly, a simple two-phase model is proposed to explain the inverse Hall–Petch effect. The model suggests that the nanocrystalline material consists of the grain-boundary and the grain interior with different mechanical properties. Secondly, a computational method is developed to create more realistic grain boundaries in simulations of polycrystals. Traditionally created grain boundaries by Voronoi tessellation are too dense and do not accurately reflect the reality and the experimental results. The strength of the nanocrystalline aluminum thin film is simulated using molecular dynamics. The grain size dependence of the elastic modulus, ultimate tensile stress, and engineering yield strength is demonstrated. The experimental values of modulus and strength are approximately 6 times smaller probably due to the presence of porosity in the real sample. An approach is developed to introduce porosity in the simulated samples at the grain boundaries and in the grain interior to reduce this discrepancy. The modeled value of modulus for a grain size of 40 nm without porosity is 67 GPa, whereas, with 50% grain-boundary porosity and 20% intra-granular porosity it is 30 GPa. For the ultimate tensile strength, we get 3 GPa and 1.4 GPa for samples without porosity and with porosity, respectively. The simulated values of modulus with porosity are still 4 times higher and the values of strength are about 2 times higher than the experimental ones. The amount of porosity in our method can be adjusted to fit the experimental values; however, high values of porosity cause the mechanical instability of the simulated samples.
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Research context
OpenAlex topics: Microstructure and mechanical properties · Surface and Thin Film Phenomena · Metal and Thin Film Mechanics
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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