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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:拉曼检测在几个真实世界表面上使用滴眼液喷墨技术制造的简易爆炸装置(IED)材料

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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:在许多材料上检测有害物质(即化学,生物和爆炸物)的要求导致了危险检测系统的发展。这些新技术及其能力可以立即为美国军方,国家安全机构和环境反应团队努力保持人们的安全和安全。特别地,由于恐怖分子的使用不断增加,常见爆炸物和简易爆炸装置(IED)材料的检测具有激励研究旨在检测多个常见的表面上的痕量(即粒子水平)量(例如,纺织品,金属,金属,塑料,天然产品,甚至是人)。非破坏性检测技术可以检测痕量的爆炸材料;然而,在复杂的化学背景的存在下可能具有挑战性。一种增强检测注意力的一种光谱技术是拉曼。一种普遍的炸药前体材料是硝酸铵(AN)。该材料具有许多农业应用,然而它也可以用于制造IED或自制炸药(HMES)。在本文中,将使用喷墨打印机沉积已知量的AN,进入几种不同的普通材料表面(例如,木材,人的头发,纺织品,金属,塑料)。材料以显微镜图像的特征为特征,并通过收集拉曼光谱数据。在本报告中,将证明对其的检测和识别。

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