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Raman Detection of improvised explosive device (IED) material fabricated using drop-on-demand InkJet Technology on several real world surfaces

机译:使用按需喷墨技术在多个实际表面上制造的简易爆炸装置(IED)材料的拉曼检测

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摘要

The requirement to detect hazardous materials (i.e., chemical, biological, and explosive) on a host of materials has led to the development of hazard detection systems. These new technologies and their capabilities could have immediate uses for the US military, national security agencies, and environmental response teams in efforts to keep people secure and safe. In particular, due to the increasing use by terrorists, the detection of common explosives and improvised explosive device (IED) materials have motivated research efforts toward detecting trace (i.e., particle level) quantities on multiple commonly encountered surfaces (e.g., textiles, metals, plastics, natural products, and even people). Non-destructive detection techniques can detect trace quantities of explosive materials; however, it can be challenging in the presence of a complex chemical background. One spectroscopic technique gaining increased attention for detection is Raman. One popular explosive precursor material is ammonium nitrate (AN). The material AN has many agricultural applications, however it can also be used in the fabrication of IEDs or homemade explosives (HMEs). In this paper, known amounts of AN will be deposited using an inkjet printer into several different common material surfaces (e.g., wood, human hair, textiles, metals, plastics). The materials are characterized with microscope images and by collecting Raman spectral data. In this report the detection and identification of AN will be demonstrated.
机译:检测多种材料上有害物质(即化学,生物和爆炸物)的要求导致了危害检测系统的发展。这些新技术及其功能可以立即用于美国军事,国家安全机构和环境响应小组,以确保人们的安全。尤其是,由于恐怖分子的使用越来越多,对普通爆炸物和简易爆炸装置(IED)材料的检测已激发了研究努力,以检测多种常见表面(例如,纺织品,金属,塑料,天然产品,甚至人)。无损检测技术可以检测痕量爆炸物;但是,在复杂的化学背景下可能具有挑战性。拉曼光谱法是一种越来越引起人们关注的光谱技术。一种流行的爆炸性前体材料是硝酸铵(AN)。材料AN具有许多农业应用,但也可用于制造简易爆炸装置或自制炸药(HME)。在本文中,已知量的AN将使用喷墨打印机沉积到几种不同的常用材料表面(例如,木材,人发,纺织品,金属,塑料)中。通过显微镜图像和收集拉曼光谱数据对材料进行表征。在此报告中,将演示对AN的检测和识别。

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