Automatic recognition of defects in plasma-facing material using image processing technology

Observing and analyzing surface images is critical for studying the interaction between plasma and irradiated plasma-facing materials.This paper presents a method for the automatic recognition of bubbles in transmission electron microscope(TEM)images of W nanofibers using image processing techniques and convolutional neural network(CNN).We employ a three-stage approach consisting of Otsu,local-threshold,and watershed segmentation to extract bubbles from noisy images.To address over-segmentation,we propose a combination of area factor and radial pixel intensity scanning.A CNN is used to recognize bubbles,outperforming traditional neural network models such as Alex Net and Google Net with an accuracy of 97.1%and recall of 98.6%.Our method is tested on both clear and blurred TEM images,and demonstrates humanlike performance in recognizing bubbles.This work contributes to the development of quantitative image analysis in the field of plasma-material interactions,offering a scalable solution for analyzing material defects.Overall,this study's findings establish the potential for automatic defect recognition and its applications in the assessment of plasma-material interactions.This method can be employed in a variety of specialties,including plasma physics and materials science.

Microscopic Damage of Tungsten and Molybdenum Exposed to Low-Energy Helium Ions

Polycrystalline tungsten(W)and molybdenum(Mo)materials both non-annealed and annealed at temperatures of 800-1750~C have been irradiated with low-energy(220 eV),high-flux(~10^(21)ions/m^2.s)He^+at an irradiation temperature of 600℃and at a dose of1.0×10^(25)ions/m^2.This non-destructive conductive atomic force microscopy technique provides direct observation and comparison of surface swellings with growth of nanoscale defects in the irradiated materials.A coral-like surface structure and nanostructured defects were formed in W when irradiated at a He+dose of 1.0×10^(25)ions/m^2.Increasing the annealing temperature resulted in an increase in the size of nanostructured defects and serious surface damage of W.Compared to W,Mo suffered much less surface damage after being irradiated at various He^+doses.

W fuzz layers:very high resistance to sputtering under fusion-relevant He+irradiations

In this study,we have modeled the sputtering process of energetic He;ions colliding with W nano-fuzz materials,based on the physical processes,such as the collision and diffusion of energetic particles,sputtering and redeposition.Our modeling shows that the fuzzy nanomaterials with a large surface-to-volume ratio exhibit very high resistance to sputtering under fusion-relevant He;irradiations,and their sputtering yields are mainly determined by the thickness of fuzzy nano0materials,the reflection coefficients and mean free paths of energetic particles,surface sputtering yields of a flat base material,and the geometry of nano-fuzz.Our measurements have confirmed that the surface sputtering yield of a W nano-fuzz layer with the columnar geometry of nano-fuzz in cross-section is about one magnitude of order lower than the one of smooth W substrates.This work provides a complete model for energetic particles colliding with the nano-fuzz layer and clarifies the fundamental sputtering process occurring in the nano-fuzz layer.

氦离子辐照下钨纳米丝的自保护行为

采用低能量(200 eV)大流强的He+辐照多晶钨材料,辐照温度为1023 K和1373 K,辐照剂量为1.0×10^25~1.0×10^26ions/m^2。用称重、扫描电子显微镜、导电原子力显微镜等手段分析辐照后钨材料的质量损失、表面形貌和内表面缺陷分布,研究了刻蚀速率与表面形貌的关系。结果表明,具有粗糙钨纳米丝表面的钨样品刻蚀速率只有平滑表面的30%。其原因是,在大流强He+辐照下钨表面纳米丝的形成阻碍钨原子的溅射。这也意味着,钨纳米丝表面的形成可作为钨材料的自保护结构层,抑制ITER相关辐照下的强刻蚀。

低能大流强氢离子辐照对钨的刻蚀行为

在聚变相关的钨(W)偏滤器辐照下,研究了低能大流强氢(H)离子辐照对多晶钨材料的刻蚀行为。使用扫描电子显微镜(SEM)、导电原子力显微镜和基于SEM的电子背散射衍射等手段研究了大流强(~1022ions/m2·s)、剂量为1.0×1026ions/m2、能量为5~200 eV的氢离子辐照对多晶W材料表面刻蚀行为的影响。结果表明,随着H离子辐照能量的增加W的溅射率迅速提高,W表面发生刻蚀后产生条纹状结构,而且同一晶粒上条纹的方向具有一致性,条纹两侧的缺陷分布明显不同,意味着W表面的刻蚀优先沿某一特定晶面方向进行。