2018 Issue

61 Over time, CCSEM expanded beyond inclusions into many and varied fields. Gun Shot Residue When a gun discharges, the firing pin strikes the cartridge primer cap, igniting it and expelling a high temperature plume of vapor and matter. This cloud rapidly condenses into fine, often spherical particulates (Fig. 2) called primer Gun-Shot Residue (GSR), which land on surrounding objects such as hands, clothing, and vehicles. Collecting and analyzing GSR may potentially link a suspect to a crime. A standard forensic method collects GSR using adhesive stubs, which are analyzed by CCSEM. Signature elements lead (Pb), antimony (Sb) and barium (Ba) are used to classify GSR. The imaged spherical shape, typi- cal of condensed particulates, provides additional data consistent with GSR. Prior to CCSEM, atomic absorption (AA) was commonly used to analyze repetitive, well-defined task. Dr. Lee outfitted an SEM with computers, digital beam control and rapid data storage, essentially creating the field of comput- er-controlled SEM (CCSEM). This technology can detect and measure thousands of features per hour, compared to a manual rate of 20 – 40. A sample is imaged in the backscattered electron mode, providing good con- trast between features and background. The beam scans the surface using an algorithm to rapidly detect features. Then, a finer scanning pattern allows the measurement of size and shape, to acquire an accurate image. Optional EDS spectra provide compositional data. Steel Cleanliness A version of CCSEM, the “Automated Steel Cleanliness Analysis Tool” (ASCAT), was programmed specifically to rapidly characterize inclusions. Shown in Figure 1, ASCAT classified inclusions from multiple heats of steel into categories: oxides, sulfides, aluminides, etc. Results were correlated with stable/unstable conditions during continuous casting of molten steel, and with corresponding equipment damage. Inclusions were graphed in ternary composition space, with calcium (Ca), sulfur (S) and aluminum (Al) at the vertices. Each dot represents an inclusion whose location in the plot is governed by EDS composition. Different point cloud patterns emerged. Erosion correlated directly with high-calcium inclusions, while excessive clogging was related to numerous aluminum oxides. Controlling inclu- sions during casting maintained steel quality with less equipment downtime. What do these have in common? • Inclusions in steel • Gun-Shot Residue for criminal cases • Powders for Additive Manufacturing • Foreign Particulate Matter in pharmaceuticals/food • Airborne particulates These materials span many diverse fields, from medicine to metals, from Additive/3D Printing to contaminants. How are they similar? They consist of particles in a range of sizes, from easily visible to microscopic. Their presence and attributes (size, shape, composi- tion, number) can affect our health, govern the performance of parts, con- taminate products, and even provide trace evidence for criminal investiga- tions. They are also alike in how these particulates can be detected and mea- sured; scanning electron microscopy (SEM) is a powerful and effective tool often used across industries to provide this critical information. Introduction to CCSEM SEM has long been a versatile method to analyze fine particulates. It pro- vides excellent surface detail, various contrast modes, a wide magnification range, and compositional data via energy dispersive X-ray spectroscopy (EDS). However, characterizing particu- lates may require analysis of hundreds or even thousands of individual parti- cles to generate statistically significant data. Accordingly, manual analysis is very slow. In the 1980s, Dr. Richard Lee, then at the U.S. Steel Research Laboratory, faced the task of manually analyzing 6000 non-metallic inclusions in steel alloys. Non-metallic inclusions are, in general, undesirable particles distrib- uted throughout a steel that negative- ly affect strength, ductility and fracture properties. After 200 inclusions, he realized a faster method was clearly needed. Computers were a natural solution to accelerate analysis for inclusion analysis because it is a highly Particulates: The Good and the Bad Amber Dalley with Stephen Kennedy and Karen Smith Fig 2 Gun shot residue (GSR) particle and EDS compositional spectrum Fig 1 ASCAT inclusion data correlation with performance in continuous caster