Oxide dispersion strengthened (ODS) alloys are desirable for high temperature applications, as dispersed oxide particles within the metal matrix act as barriers to dislocation motion and grain growth at elevated temperatures. Traditional processing routes for ODS alloys are powder metallurgy based, utilizing mechanical alloying by high energy ball milling to mix metal and oxide powders, necessitating compaction techniques such as hot isostatic pressing or hot extrusion to create a dense final product. Recent studies have investigated production of dispersion strengthened alloys without powder metallurgy by introducing a dispersed ceramic phase to a bulk alloy via a physical application process, such as spraying [1], followed by severe plastic deformation to refine grain size. This study proposes an ODS synthesis method of introducing oxides via selective internal oxidation, followed by equal channel angular pressing (ECAP) to refine grain size.In this work, Fe-1.5 wt% Y alloys were studied. The as-cast microstructure consisted of an a-Fe matrix and Fe 17 Y 2 intermetallic, as shown in Figure 1a. Oxides were introduced into the bulk alloy when the material underwent selective internal oxidation, during which the Fe 17 Y 2 intermetallic phase oxidized to Y 2 O 3 particles and pure Fe, leaving the a-Fe matrix unaltered [2].Internal oxidation was followed by ECAP in order to refine the matrix Fe grain size while also dispersing the Y 2 O 3 particles. During ECAP, as illustrated in Figure 1b, strain is introduced as the ingot is pressed through a die at an angle, which for this study was 90°. Because the dimensions of the cross section are the same before and after deformation, the piece can undergo multiple consecutive passes, each introducing additional plastic deformation to the sample. The processing variables studied in this work were the number of ECAP passes and the temperature at which ECAP was performed. In this study, ECAP was performed at room temperature (25°C) and an elevated temperature (350°C), for four ECAP passes and for eight ECAP passes.After ECAP, Fe grains in the matrix were imaged using electron channeling contrast via backscattered electron detection in a FEI Quanta 600 FEG-SEM. Post-ECAP grain size of the Fe matrix was measured using the ASTM E112 linear intercept method [3], finding that increased number of ECAP passes had no significant impact on grain size. A series of thermal stability anneals at 250°C, 400°C, and 1000°C were performed, and Vickers microhardness measurements taken before and after annealing found that the high-temperature ECAP samples maintained similar hardness after 400°C, while room temperature ECAP samples exhibited loss of thermal stability based on an observed significant drop in hardness after 400°C anneals.An automated high-throughput dispersion analysis program was developed to quantify particle dispersion using the quadrat method [4]. Applying the dispersion program to the ECAP samples, it was found that the room temperature ECAP samples had more dispersed particl...

The corrosion behavior of cast Mg-Al alloys such as AZ91D and AM60B is strongly dependent on the alloy composition and microstructure.  Alloy grain size, area fraction of phases and spatial distribution of the phases are important factors governing both full immersion and atmospheric corrosion behavior. Mg-Al alloy systems can be broadly characterized as being composed of a primary a-Mg matrix (grains) containing Al in solid solution and b-phase intermetallic particles mainly distributed within and adjacent to grain boundaries.  Heat treatment of the as-cast alloys significantly modifies the microstructure altering grain size, phase amounts and distribution as well as the a-Mg phase composition. The general scenario that we envision evolving during corrosion of cast Mg-Al alloys is the following. In near-neutral electrolytes containing chloride, second phase particles such as the Mg17Al12 b-phase serve as cathodes for water reduction resulting in increases in pH.  The corresponding dissolution reaction involves the dealloying of Mg from a-matrix grains resulting in enrichment and redistribution of solid solution Al as well as impurities (such as Fe) that may be present within the grains.  The redistributed Al will form Al clusters on the surface of the now dealloyed a-phase that can also serve as cathodes. If the pH rises to ~ 9-9.5, the a-phase will begin to passivate and the b-phase can in principle dealloy (selective dissolution of Al) since the corrosion potential (~1.5V vs. SCE) is high enough for this to occur.  At this elevated pH, aluminum may dissolve as the soluble aluminate and will tend to precipitate on to the dissolving alloy surface. Thus, owing to these pH changes local cathodes can become anodes and vice versa. The degree to which these processes affect corrosion behaviors depends on the spatial density (mean separation) of existing and evolving surface cathodes and how this length scale compares to the thickness of a electrolyte diffusion boundary layer or condensed liquid layer (atmospheric corrosion) and grain size. Given this hypothesis for corrosion behavior, we have been examining dealloying of AZ91D and AM60B as well the constituent a- and b-phases. Instead of relying on a technique such as EDS to characterize the time-dependent change in Al concentration on a corroding surface we developed an electrochemical assay involving Li underpotential deposition (UPD) that measures the electrochemically active surface area (ECSA) of Al. We show results comparing the new assay protocol yielding the ECSA of Al to that obtained from EDS. Anodic dissolution behaviors of the alloys and constituent phases have been measured in an ionic liquid electrolyte (neat1:2 molar ratio of choline chloride:urea) which allows for accurate determination of the evolving surface composition as a function of well-defined anodic dealloying rates. We discuss these results together with some preliminary Kinetic Monte Carlo modeling of corrosion processes of the constituent a-phase.

Oxide dispersion strengthened (ODS) alloys are composites with a high-density dispersion of oxides throughout a metallic matrix. Oxide dispersion strengthening is important for ultrafine-grained (UFG) metallic materials, which exhibit high strength as a result of submicron grain size. While UFG materials exhibit admirable properties, the high driving force for grain growth lowers their mechanical properties at high temperatures. Oxide dispersion strengthening stabilizes the UFG materials at elevated temperatures by acting as barriers to dislocation motion, limiting grain growth and creep. In this study, the term "dispersion" refers to the spatial distribution of particles in the matrix, where more dispersed particles are more uniform and less dispersed particles are more clustered. Several methods of dispersion quantification exist in the literature, but there is no single well established method for quantifying particle dispersion in ODS alloys. In developing a dispersion quantification method for ODS alloys, it is desirable to have an automated, high-throughput process that is able to quickly analyze numerous micrographs from across an entire bulk sample surface. The quadrat method was chosen for this study for its effectiveness in evaluating clustered dispersions [1], as well as its applicability to automating a high-throughput analysis program.The quadrat method analyzes dispersion by dividing images into a grid of square "quadrats" and counting the number of particles in each quadrat. In the current study, the image analysis program Fiji, a distribution of ImageJ [2], is used to automate the quadrat method in conjunction with Python scripting. The quadrat program can analyze a series of micrographs that exhibit sufficient contrast between the particles of interest and the matrix (Fig. 1a). Micrographs are recorded in a FEI Quanta 200 and FEI Nova Nanolab SEM under various acceleration voltages and electron detectors. Fiji isolates the particles in each image by applying a thresholding parameter [3] (Fig. 1b). Then, the center of mass of each particle is found, and a quadrat grid is applied (Fig. 1c). The script then counts the number of particles in each quadrat to create a histogram plotting frequency of number of particles per quadrat (Fig. 1d), which can then be fitted to statistical distribution curves in order to quantify particle dispersion. If the particles are randomly dispersed, the histogram will fit closer to a Poisson distribution, while a more clustered dispersion will fit closer to a negative binomial distribution. Additionally, a skewness parameter can be calculated from the mean and standard deviation of number of particles per quadrat, which indicates the degree of clustering. This skewness value is useful for comparing the degree of dispersion between similar samples of different processing conditions. This automated approach is applied to a series of Fe-Y alloys processed under a variety of processing conditions designed to create nanoscale Y 2 O 3 oxide particles by internal oxidation of...