Assessing automated image analysis and stable isotope signatures for small arms propellant differentiation and brand identification

As submitted by the proposer: Smalls 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). Typically, the SAP charge is loaded into a metal pipe (commonly steel) and sealed with screw-fit end caps. These devices are termed "pipe bombs," and are the most common IEDs in the United States. Two recent high-profile domestic terrorist attacks using IEDs (Boston Marathon Bombing and NY/NJ attempted bombings) further demonstrate their continued usage. In addition, U.S. and coalition war fighters combat IEDs at an unprecedented scale. Thus, there is a need to develop robust metrics for the characterization of propellants that are used as explosives, as well as for comparisons between exemplar and recovered explosive residues. The goals of the proposed research are to investigate the utility of high-throughput, low-cost quantitative automated image analysis, additive profiling and compound-specific stable isotope signatures of SAP for potential brand identification and sample discrimination. If found to be successful, the metrics will provide advanced methods for the examination of small arms propellants recovered from pre- and post-blast improvised explosive devices. The automated image analysis method is appealing since it is non-destructive and relies upon low cost analytical equipment and instrumentation. A rigorous assessment of the existing GC/MS additive profiling methodology will be achieved as well as a better understanding of its role in SAP examination. The measurement of compound-specific stable isotope signatures is considered a final tier analysis, which may provide highly discriminatory information that could constrain geographic/manufacturer-specific characteristics. The proposed research will be the first of its kind to combine uncorrelated data sets from complementary analytical methods to develop best practice recommendations for the characterization of SAP in forensic and intelligence casework. Note: This project contains a research and/or development component, as defined in applicable law. - See 2 CFR 200.210(a)(14). ca/ncf