Using the Effective Diffusivity of Polycrystals to Infer a Complete 5D Structure-Property Model for Hydrogen Diffusivity in Iron Grain Boundaries

Brigham Young University, Provo UT

Investigators

Abstract

Non-technical Abstract Grain boundaries are defects that strongly influence-and in some cases govern-the properties of engineering materials including strength, embrittlement, corrosion, solar cell efficiency, among others. For example, these defects in structural metals can act as preferential sites for corrosion and embrittlement, which can lead to catastrophic failure of engineering structures like the recent failure of the seismic safety rods in the San Francisco-Oakland Bay Bridge. In spite of their profound importance, the structure of these defects is so complex that it is extremely difficult to predict their properties. Furthermore, there are so many types of grain boundaries that isolating and testing each kind one-by-one is not feasible. Instead of testing them one-by-one, this project presents a new efficient framework called "property localization" to infer the properties simultaneously using a small number of samples. Because of its relevance to the problem of corrosion and embrittlement, this framework will be used to develop a model to predict the effects of hydrogen in iron. By combining theoretical tools from diverse fields (medicine, computer science, and materials science) with advanced computational and experimental methods and emerging microscopy techniques that permit high-throughput probing of this project will yield new insight and enable the prediction of properties. This new understanding will lead to the development of devices and engineering structures with improved safety and performance. This project is being conducted within the framework of a combined graduate and undergraduate mentoring program that is designed to increase the representation and retention of female engineering students through two primary strategies: involvement in mentored research early in their academic career, and engagement with peer-to-peer mentoring networks via a social media platform. Technical Abstract This project aims to exploit the confluence of three scientific advances to tackle a previously intractable problem: predicting the properties of grain boundaries (GBs) as a function of their five-dimensional crystallographic structure. 3D surface EBSD microscopy, polycrystalline atomistic studies, and inverse methods for deconvolution of large datasets provide the keys for this transformational opportunity. A significant obstacle to the development of GB structure-property models has been the dimensionality of the 5D GB crystallography state space and the correspondingly large number of bicrystal experiments that is required to adequately probe this space (~10^5). The proposed work will use tomographic reconstruction techniques to invert the concept of homogenization, and thereby determine structure-property relationships from known microstructures and effective property measurements. This deconvolution method is referred to as property localization and has the primary advantage of reducing the number and complexity of the required experiments by orders of magnitude. The new framework is applied to the specific problem of constructing a quantitative model for hydrogen diffusivity in iron GBs as a function of their five crystallographic parameters. This work has implications for damage and degradation, such as hydrogen embrittlement and corrosion, leading to more resistant materials and enabling the safe, reliable and efficient storage and delivery of hydrogen fuel necessary for a clean energy economy. The integration of the research with the mentoring and outreach activities to increase retention of women students will support the development of a diverse science and engineering workforce that will directly benefit society.