Characterizing nitrocellulose by nitration degree and molecular weight: A user-friendly yet powerful forensic comparison method for smokeless powders

• SEC-UV-RID for characterizing nitrocellulose in smokeless powders. • Simultaneous determination of molecular-weight distribution and nitration degree. • Characterizing nitrocellulose offers additional data next to the additive profile. • Method achieves high discriminating powers for two uncorrelated features. • Requires little financial investment with regards to instruments and maintenance. Smokeless powders (SPs) are commonly used as propellant for ammunition but are also used as explosive component in improvised explosive devices. Therefore, its chemical characterization is of high importance when reporting forensic explosives investigations to a court of law. While conventional analytical strategies focus on the characterization of the additives in SPs, only few methods consider the main explosive component in SP, nitrocellulose (NC). In this study, a user-friendly analytical method was developed for characterizing NC in SPs. The method employs size-exclusion chromatography combined with ultraviolet (UV) and refractive index (RI) detection. Through the size separation, the SP additives are separated from the polymeric NC because of their lower molecular weight. The novel part of the system lies in the simultaneous measurement of the molecular-weight distribution (MWD) and the nitration degree, thus boosting the discriminating power obtained from a single analysis. The combined use of two detectors enables a highly specific analysis, because the ratio between the UV and RI signal intensities is directly correlated with the nitration degree of NC. Characterizing the nitrocellulose demonstrated high discriminating powers of 98.95 % for the MWD and 92.65 % for the nitration degree using a set of 20 SP products. Combined, all sample pairs in our sample set could be distinguished. The proposed method is not limited to product classification that is obtained from additive profiles, and potentially offers individualization at batch level. This method provides high discriminating power while requiring little financial investment with regards to instrumentation and maintenance.