识别表面形态特征以区分由重铀酸铵和过氧化铀酰合成的 U3O8 混合物,Radiochimica Acta

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识别表面形态特征以区分由重铀酸铵和过氧化铀酰合成的 U3O8 混合物
Radiochimica Acta ( IF 1.4 ) Pub Date : 2019-12-18 , DOI: 10.1515/ract-2019-3140
Sean T. Heffernan ₁ , Nhat-Cuong Ly ₂ , Brock J. Mower ₁ , Clement Vachet ₂ , Ian J. Schwerdt ₁ , Tolga Tasdizen ₂ , Luther W. McDonald IV _{1,

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Affiliation

摘要在本研究中，研究了由过氧化铀 (UO4) 和重铀酸铵 (ADU) 合成的八氧化三铀 (U3O8) 混合物的表面形态差异。使用粉末 X 射线衍射 (p-XRD) 验证每个样品的纯度，并收集扫描电子显微镜 (SEM) 图像以识别独特的形态特征。发现来自 ADU 和 UO4 的 U3O8 是独一无二的。定性地说，这两种颗粒具有相似的特征，主要是圆形。使用材料形态分析 (MAMA) 软件，对颗粒形状和尺寸进行量化。发现 UO4 产生的 U3O8 颗粒是 ADU 产生的颗粒面积的三倍。随着起始形态的量化，来自 ADU 和 UO4 的 U3O8 样品以已知数量物理混合。收集混合样品的 SEM 图像，并使用 MAMA 软件量化颗粒属性。由于来自 ADU 的 U3O8 颗粒与 UO4 不同，因此可以使用 SEM 成像结合颗粒分析来量化混合物的组成。这提供了一种量化铀氧化物混合物的加工历史的新方法。机器学习还用于通过使用基于卷积神经网络 (CNN) 的深度学习技术的直接分类和粒子分割来帮助进一步量化图像数据库中的特征。这表明这些技术可以高精度地区分混合物，并显示出混合物之间形态的显着差异。

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Identifying surface morphological characteristics to differentiate between mixtures of U3O8 synthesized from ammonium diuranate and uranyl peroxide

Abstract In the present study, surface morphological differences of mixtures of triuranium octoxide (U3O8), synthesized from uranyl peroxide (UO4) and ammonium diuranate (ADU), were investigated. The purity of each sample was verified using powder X-ray diffractometry (p-XRD), and scanning electron microscopy (SEM) images were collected to identify unique morphological features. The U3O8 from ADU and UO4 was found to be unique. Qualitatively, both particles have similar features being primarily circular in shape. Using the morphological analysis of materials (MAMA) software, particle shape and size were quantified. UO4 was found to produce U3O8 particles three times the area of those produced from ADU. With the starting morphologies quantified, U3O8 samples from ADU and UO4 were physically mixed in known quantities. SEM images were collected of the mixed samples, and the MAMA software was used to quantify particle attributes. As U3O8 particles from ADU were unique from UO4, the composition of the mixtures could be quantified using SEM imaging coupled with particle analysis. This provides a novel means of quantifying processing histories of mixtures of uranium oxides. Machine learning was also used to help further quantify characteristics in the image database through direct classification and particle segmentation using deep learning techniques based on Convolutional Neural Networks (CNN). It demonstrates that these techniques can distinguish the mixtures with high accuracy as well as showing significant differences in morphology between the mixtures. Results from this study demonstrate the power of quantitative morphological analysis for determining the processing history of nuclear materials.

更新日期：2019-12-18

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