Elemental Characterization of Leaded and Lead-Free Inorganic Primer Gunshot Residue Standards Using Single Particle Inductively Coupled Plasma Time-of-Flight Mass Spectrometry

Abstract

This study describes the use of single particle inductively coupled plasma time-of-flight mass spectrometry (spICP-TOFMS) for the detection and classification of inorganic gunshot residue (IGSR) particles. To establish reliable multi-element criteria to classify IGSR particles, leaded and lead-free IGSR reference materials were analyzed, and the elemental compositions of the individual particles were quantified. The results suggest that expanded element compositions may be used to classify IGSR particles via spICP-TOFMS compared to those used in conventional IGSR analysis using scanning electron microscopy energy dispersive X-ray spectroscopy (SEM-EDS). For spICP-TOFMS analysis of leaded IGSR particles, classification may be based on the presence of lead (Pb), antimony (Sb), and barium (Ba) just as in SEM-EDS; however, additional particle types, such as lead-copper (Pb–Cu) particles, contribute significantly (∼30%) to the leaded IGSR particle population. In lead-free IGSR particles, the dominate multi-metal particle composition found is titanium–zinc (Ti–Zn) with a conserved Zn:Ti ratio of 1.4:1, but other elements, such as copper (Cu), are also characteristic. In mixtures of the two IGSR reference materials, we were able to classify over 80% of the multi-metal particles detected with no false-positive particle-type assignments. With spICP-TOFMS, particles smaller than those typically measured by SEM-EDS are detected, with estimated median diameters for leaded and lead-free IGSR of 180 and 320 nm, respectively. Through measuring these smaller particles, up to ∼two times more particles per mL are recorded by spICP-TOFMS compared to that found by SEM-EDS. Overall, high-sensitivity and high-throughput analysis using spICP-TOFMS enables quantitative, rapid multi-elemental characterization, and classification of individual IGSR particles.

Graphical Abstract

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