Microtrace Scientists Present at Pittcon 2020
Pittcon 2020 Conference and Expo was held in Chicago, IL this week (March 1-5, 2020).
On Monday, March 2nd, Microtrace Senior Research Microscopists Christopher Palenik and Jack Hietpas presented papers at the National Institute of Justice’s Advancements in the Analysis of Forensic Evidence session of the conference. Dr. Palenik presented on “Developments in the Forensic Analysis of Automotive Paints by SEM/EDS,” and Dr. Hietpas spoke on “The Chemical, Micro-morphological and Isotopic Characterization of Smokeless Powders.”
Their work on these subjects has been supported by grants from the National Institute of Justice.
Developments in the Forensic Analysis of Automotive Paints by SEM/EDS
Christopher Palenik*, Ethan Groves, and Skip Palenik
The forensic comparison of paint based upon elemental analysis is probative; however, the present ASTM guide for SEM/EDS in forensic paint analysis provides little guidance for the impact that sample preparation, analytical parameters, and data evaluation have can have on results. While both the theoretical underpinnings for the elemental analysis of materials by SEM/EDS and a practical approach to the forensic comparison of paints are well-established in the literature, the theoretical and practical have never been married. For example, forensic paint samples are often prepared with little attention to the impact of sample preparation; the analysis of a paint layers are conducted with limited considerations to the impact of analytical parameters; and the criteria for associating or excluding samples compared by elemental analysis utilizes a subjective comparison with little context for establishing the significance of results. This talk will present a review of the current ASTM approach to the forensic comparison of paint by SEM/EDS and the lessons extracted from a directed analysis of 300 automotive paints comprised of ~1200 layers. These findings provide more detailed guidance for the forensic analysis of paint by SEM/EDS, a broader understanding of automotive paint compositions, and a potentially more rigorous and objective approach to forensic paint comparison.
The Chemical, Micro-morphological and Isotopic Characterization of Smokeless Powders
Jack Hietpas*, Claire Page, Casey Brown, Devin Kress, Todd Sowers, Ryan Schonert, and Wayne Moorehead
Small arms propellants (SAP) are readily accessible and cost-effective materials that firearms enthusiasts can acquire for the legitimate assembly of ammunition. Unfortunately, the ease of access to and low cost of these materials is advantageous for their utilization in the construction of improvised explosive devices (IEDs). Thus, there is a need to develop robust metrics for the characterization of SAP. The goal of the presented research is to investigate the potential for SAP brand identification and sample discrimination. To accomplish this goal, we are combining the results from three data streams (powder micromorphology, additive profiling, and stable isotope signatures). Samples of SAP were acquired and represent a wide range of different distributors (n=8) and brands (n=160).
By relying on the non-destructive method of particle micromorphometry, the success rate for brand identification is ~80%. The results show that samples are primarily differentiated using size-dependent parameters, with shape parameters providing limited separation. Of the 110 samples analyzed by GC/MS to most common additives include nitroglycerin, diphenylamine, dibutyl phthalate, and ethyl centralite. Approximately 70% of the profiles were the same as at least one other profile. It was noted that many samples that gave the same additive profiles could be differentiated by micromorphology. Nitrocellulose isotopic compositions were examined to determine if there were recognizable geographic signatures. SAP manufactured in Canada and Belgium cluster in the most negative δ13C and δ15N space, Finnish cluster with negative δ13C and positive δ15N, Swedish samples cluster in the center of the data set, US and Australian tend to show the least negative δ13C compositions and have positive δ15N signatures.
The results from this study show that there is a need for the fusion of data from different analytical methods for more robust sample differentiation and comparison.
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