Wang Yuliang, Wang Huimin, Bi Shusheng, Guo Bin
School of Mechanical Engineering and Automation, Beihang University, Beijing 100191, P.R. China.
Department of Materials Science and Engineering, The Ohio State University, 2041 College Rd., Columbus, OH 43210, USA.
Beilstein J Nanotechnol. 2015 Apr 14;6:952-63. doi: 10.3762/bjnano.6.98. eCollection 2015.
Nanobubbles (NBs) on hydrophobic surfaces in aqueous solvents have shown great potential in numerous applications. In this study, the morphological characterization of NBs in AFM images was carried out with the assistance of a novel image segmentation method. The method combines the classical threshold method and a modified, active contour method to achieve optimized image segmentation. The image segmentation results obtained with the classical threshold method and the proposed, modified method were compared. With the modified method, the diameter, contact angle, and radius of curvature were automatically measured for all NBs in AFM images. The influence of the selection of the threshold value on the segmentation result was discussed. Moreover, the morphological change in the NBs was studied in terms of density, covered area, and volume occurring during coalescence under external disturbance.
水性溶剂中疏水表面上的纳米气泡（NBs）在众多应用中已显示出巨大潜力。在本研究中，借助一种新颖的图像分割方法对原子力显微镜（AFM）图像中的纳米气泡进行了形态表征。该方法将经典阈值法与一种改进的主动轮廓法相结合，以实现优化的图像分割。比较了用经典阈值法和所提出的改进方法获得的图像分割结果。使用改进方法，可以自动测量AFM图像中所有纳米气泡的直径、接触角和曲率半径。讨论了阈值选择对分割结果的影响。此外，还从外部干扰下聚并过程中纳米气泡的密度、覆盖面积和体积方面研究了其形态变化。
