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 ...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.展开更多
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 transmissi...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.展开更多
基金supported by the National Key R&D Program of China(No.2017YFE0300106)National Natural Science Foundation of China(No.11320101005)+1 种基金Liaoning Provincial Natural Science Foundation(Nos.20180510006,2019-ZD0186)Natural Science Basis Research Program of Shanxi Province(No.2020GY-268)。
文摘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.
基金supported by the National Key R&D Program of China(No.2017YFE0300106)Dalian Science and Technology Star Project(No.2020RQ136)+1 种基金the Central Guidance on Local Science and Technology Development Fund of Liaoning Province(No.2022010055-JH6/100)the Fundamental Research Funds for the Central Universities(No.DUT21RC(3)066)。
文摘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.
