Van 't Hoff Institute for Molecular Sciences, Faculty of Science, Amsterdam University of Amsterdam, the Netherlands; Netherlands Forensic Institute, The Hague, the Netherlands. Electronic address: [Email]
Erythritol tetranitrate (ETN) was prepared independently by two research groups from the USA and the Netherlands. The partially nitrated impurities present in ETN were studied using liquid chromatography-mass spectrometry to address the ultimate challenge in forensic explosives investigations, i.e., providing chemical and tactical information on the production and origin of the explosive material found at a crime scene. Accurate quantification of the tri-nitrated byproduct erythritol trinitrate (ETriN) was achieved by in-lab production of an ETriN standard and using custom-made standards of the two isomers of ETriN (1,2,3-ETriN and 1,2,4-ETriN). Large differences in levels of ETriN were observed between the two sample sets showing that, even when similar synthesis routes are employed, batches from different production locations can contain different impurity profiles. In one of the sample sets the ratios of the lesser partially nitrated impurities, EDiN and EMN, in the ETN samples could be determined. The impurity profiles enable prediction of post-synthesis work-up steps by reduction of the level of partially nitrated products upon recrystallization. However, impurity analysis does not enable predictions with respect to raw material or synthesis route used. Nonetheless, characteristic impurity profiles obtained can be used in forensic casework to differentiate or link ETN samples. However, forensic interpretation can be complicated by acid catalyzed degradation which can cause changes in impurity levels over time. The high food-grade quality of the erythritol precursor materials did not provide other impurity markers using the LC-MS methods in this study. To expand our framework of chemical attribution a follow-up study will be reported that focuses on stable isotope analysis of ETN and its precursor materials that potentially allow predictions for forensic explosives intelligence.