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Rapid Nuclear Forensics Analysis via Laser Based Microphotonic Techniques Coupled with Chemometrics

机译:通过基于激光的微光电技术快速核法医学分析,与化学计量学相结合

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Nuclear forensics (NF) is an important tool for analysis and attribution of nuclear and radiological materials (NRM) in support of nuclear security. The critical challenge in NF currently is the lack of suitable microanalytical methodologies for direct, rapid and minimally-invasive detection and quantification of NF signatures. Microphotonic techniques can achieve this task particularly when the materials are of limited size and under concealed condition. The purpose of this paper is to demonstrate the combined potential of chemometrics enabled LIBS and laser Raman spectromicroscopy (LRS) for rapid NF analysis and attribution. Using LIBS, uranium lines at 385.464 nm, 385.957 nm and 386.592 nm were identified as NF signatures in uranium ore surrogates. A multivariate calibration strategy using artificial neural network was developed for quantification of trace uranium. Principal component analysis (PCA) of LIBS spectra achieved source attribution of the ores. LRS studies on UCl3, UO3(NO3)2.6H2O, UO2SO4.3H2O and UO3 in pellet state identified the bands associated with different uranium molecules as varying in the range of (840 to 867) ± 15 cm~(-1). Using this signature, we have demonstrated spectral imaging of uranium under concealed conditions.
机译:核法医(NF)是用于核安全支持核和放射材料(NRM)的分析和归因的重要工具。 NF中的临界挑战目前是缺乏合适的微量微分内容方法,用于直接,快速和微创检测和量化NF签名的定量。特别是当材料尺寸有限和隐藏条件下的材料时,缩放技术可以实现这项任务。本文的目的是展示化学计量学的组合潜力,使LIBS和激光拉曼光谱测定(LRS)进行快速分析和归因。使用LIBS,385.464nm,385.957nm和386.592nm的铀系被鉴定为铀矿孕代的NF签名。开发了使用人工神经网络的多变量校准策略用于量化痕量铀。 Libs Spectra的主成分分析(PCA)实现了矿石的源归因。 LRS研究UCl3,UO 3(NO 3)2.6H2O,UO2SO4.3H2O和颗粒状态的UO 3鉴定了与不同铀分子相关的条带在(840至867)±15cm〜(-1)的范围内。使用此签名,我们在隐藏条件下展示了铀的光谱成像。

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