Segmentation of clustered cells in negative phase contrast images with integrated light intensity and cell shape information

Laser Controlled Manipulation of Microbubbles on a Surface with Silica-Coated Gold Nanoparticle Array

Microbubble generation and manipulation play critical roles in diverse applications such as microfluidic mixing, pumping, and microrobot propulsion. However, existing methods are typically limited to lateral movements on customized substrates or rely on specific liquids with particular properties or designed concentration gradients, thereby hindering their practical applications. To address this challenge, this paper presents a method that enables robust vertical manipulation of microbubbles. By focusing a resonant laser on hydrophilic silica-coated gold nanoparticle arrays immersed in water, plasmonic microbubbles are generated and detach from the substrates immediately upon cessation of laser irradiation. Using simple laser pulse control, it can achieve an adjustable size and frequency of bubble bouncing, which is governed by the movement of the three-phase contact line during surface wetting. Furthermore, it demonstrates that rising bubbles can be pulled back by laser irradiation induced thermal Marangoni flow, which is verified by particle image velocimetry measurements and numerical simulations. This study provides novel insights into flexible bubble manipulation and integration in microfluidics, with significant implications for various applications including mixing, drug delivery, and the development of soft actuators.

Automated image segmentation-assisted flattening of atomic force microscopy images

Atomic force microscopy (AFM) images normally exhibit various artifacts. As a result, image flattening is required prior to image analysis. To obtain optimized flattening results, foreground features are generally manually excluded using rectangular masks in image flattening, which is time consuming and inaccurate. In this study, a two-step scheme was proposed to achieve optimized image flattening in an automated manner. In the first step, the convex and concave features in the foreground were automatically segmented with accurate boundary detection. The extracted foreground features were taken as exclusion masks. In the second step, data points in the background were fitted as polynomial curves/surfaces, which were then subtracted from raw images to get the flattened images. Moreover, sliding-window-based polynomial fitting was proposed to process images with complex background trends. The working principle of the two-step image flattening scheme were presented, followed by the investigation of the influence of a sliding-window size and polynomial fitting direction on the flattened images. Additionally, the role of image flattening on the morphological characterization and segmentation of AFM images were verified with the proposed method.