Search

U-10 wt.% Zr (U-10Zr) metallic fuel is the leading candidate for next-generation sodium-cooled fast reactors. Porosity is one of the most important factors that impacts the performance of U-10Zr metallic fuel. The pores generated by the fission gas accumulation can lead to changes in thermal conductivity, fuel swelling, Fuel-Cladding Chemical Interaction (FCCI) and Fuel-Cladding Mechanical Interaction (FCMI). Therefore, it is crucial to accurately segment and analyze porosity to understand the U-10Zr fuel system to design future fast reactors. To address the above issues, we introduce a workflow to process and analyze multi-source Scanning Electron Microscope (SEM) image data. Moreover, an encoder-decoder-based, deep fully convolutional network is proposed to segment pores accurately by integrating the residual unit and the densely-connected units. Two SEM 250 × field of view image datasets with different formats are utilized to evaluate the new proposed model's performance. Sufficient comparison results demonstrate that our method quantitatively outperforms two popular deep fully convolutional networks. Furthermore, we conducted experiments on the third SEM 2500 × field of view image dataset, and the transfer learning results show the potential capability to transfer the knowledge from low-magnification images to high-magnification images. Finally, we use a pre-trained network to predict the pores of SEM images in the whole cross-sectional image and obtain quantitative porosity analysis. Our findings will guide the SEM microscopy data collection efficiently, provide a mechanistic understanding of the U-10Zr fuel system and bridge the gap between advanced characterization to fuel system design. [ABSTRACT FROM AUTHOR]

Gaseous fission products from nuclear fission reactions tend to form fission gas bubbles of various shapes and sizes inside nuclear fuel. The behavior of fission gas bubbles dictates nuclear fuel performances, such as fission gas release, grain growth, swelling, and fuel cladding mechanical interaction. Although mechanical understanding of the overall evolution behavior of fission gas bubbles is well known, lacking the quantitative data and high-level correlation between burnup/temperature and microstructure evolution blocks the development of predictive models and reduces the possibility of accelerating the qualification for new fuel forms. Historical characterization of fission gas bubbles in irradiated nuclear fuel relied on a simple threshold method working on low-resolution optical microscopy images. Advanced characterization of fission gas bubbles using scanning electron microscopic images reveals unprecedented details and extensive morphological data, which strains the effectiveness of conventional methods. This paper proposes a hybrid framework, based on digital image processing and deep learning models, to efficiently detect and classify fission gas bubbles from scanning electron microscopic images. The developed bubble annotation tool used a multitask deep learning network that integrates U-Net and ResNet to accomplish instance-level bubble segmentation. With limited annotated data, the model achieves a recall ratio of more than 90%, a leap forward compared to the threshold method. The model has the capability to identify fission gas bubbles with and without lanthanides to better understand the movement of lanthanide fission products and fuel cladding chemical interaction. Lastly, the deep learning model is versatile and applicable to the micro-structure segmentation of similar materials. [ABSTRACT FROM AUTHOR]

Somatic embryogenesis is the most common regeneration method for the application of new genomic techniques like cisgenesis/intragenesis, genome editing, and RNAi. However, some local important genotypes show recalcitrance to this morphogenetic strategy, which represents an obstacle for the application of genetic engineering techniques. Whole flowers, stamens, and pistils of three different Italian Vitis vinifera L. cultivars (Ancellotta, Glera, and Lambrusco Salamino), and four hybrid rootstocks (110 Richter, 17.37, SO4, Star 50) have been tested in several culture media with changing basal salts (NN and MS), different combinations of growth regulators (BAP, 2,4-D, NOA, PIC, and NAA), and gelling agents, to initiate somatic embryogenesis. The formation of embryogenic calli was observed mainly from whole flowers cultured on PIV medium (NN salts, B5 vitamins, 3 g L−1 gelrite, 60 g L−1 sucrose, 8.9 µM BAP, and 4.5 µM 2,4-D), and stamens on MS1 medium (MS salts and vitamins, 7 g L−1 plant agar, 20 g L−1 sucrose, 4.5 µM BAP, and 5 µM 2,4-D), in the cv. Ancellotta, Lambrusco Salamino, and all the rootstocks, except for Star 50, which showed the best embryogenetic response from pistils placed on MS1. In a recalcitrant cv. as Glera, pistils placed on MS medium supplemented with 1 µM BAP, 5 µM 2,4-D, and gelrite as gelling agent, showed the highest percentage of embryogenesis. In addition, a two-step protocol was efficiently optimized for further induction of secondary embryo production for the above-listed grapevine genotypes, which guaranteed the long-term maintenance of embryogenic cultures from clusters or single somatic embryos. Key message: Different types of explants, induction media combinations and concentrations influence the efficiency of regeneration via somatic embryogenesis in recalcitrant Vitis cultivars and rootstocks. [ABSTRACT FROM AUTHOR]