VISION-iT:A Framework for Digitizing Bubbles and Droplets

Quantifying the nucleation processes involved in liquid-vapor phase-change phenomena,while dauntingly challenging,is central in designing energy conversion and thermal management systems.Recent technological advances in the deep learning and computer vision field offer the potential for quantifying such complex two-phase nucleation processes at unprecedented levels.By leveraging these new technologies,a multiple object tracking framework called“vision inspired online nuclei tracker(VISION-iT)”has been proposed to extract large-scale,physical features residing within boiling and condensation videos.However,extracting high-quality features that can be integrated with domain knowledge requires detailed discussions that may be field-or case-specific problems.In this regard,we present a demonstration and discussion of the detailed construction,algorithms,and optimization of individual modules to enable adaptation of the framework to custom datasets.The concepts and procedures outlined in this study are transferable and can benefit broader audiences dealing with similar problems.

DropletMask: Leveraging visual data for droplet impact analysis

Machine learning-assisted computer vision represents a state-of-the-art technique for extracting meaningful features from visual data autonomously.This approach facili-tates the quantitative analysis of images,enabling object detection and tracking.In this study,we utilize advanced computer vision to precisely identify droplet motions and quantify their impact forces with spatiotemporal resolution at the picoliter or millisecond scale.Droplets,captured by a high-speed camera,are denoised through neuromorphic image processing.These processed images are employed to train con-volutional neural networks,allowing the creation of segmented masks and bounding boxes around moving droplets.The trained networks further digitize time-varying multi-dimensional droplet features,such as droplet diameters,spreading and sliding motions,and corresponding impact forces.Our innovative method offers accurate measurement of small impact forces with a resolution of approximately 10 pico-newtons for droplets in the micrometer range across various configurations with the time resolution at hundreds of microseconds.

Fabrication of patterned graphitized carbon wires using low voltage near-field electrospinning, pyrolysis, electrodeposition, and chemical vapor deposition

We herein report a high-resolution nanopatterning method using low voltage electromechanical spinning with a rotating collector to obtain aligned graphitized micro and nanowires for carbon nanomanufacturing.A small wire diameter and a small inter-wire spacing were obtained by controlling the electric field,the spinneret-to-collector distance,the pyrolysis parameters,the linear speed of the spinneret,the rotational speed of the collector.Using a simple scaling analysis,we show how the straightness and the diameter of the wires can be controlled by the electric field and the distance of the spinneret to the collector.A small inter-wire spacing,as predicted by a simple model,was achieved by simultaneously controlling the linear speed of the spinneret and the rotational speed of the collector.Rapid drying of the polymer nanowires enabled the facile fabrication of suspended wires over various structures.Patterned polyacrylonitrile wires were carbonized using standard stabilization and pyrolysis to obtain carbon nanowires.Suspended carbon nanowires with a diameter of<50 nm were obtained.We also established a method for making patterned,highly graphitized structures by using the aforementioned carbon wire structures as a template for chemical vapor deposition of graphite.This patterning technique offers high throughput for nano writing,which outperforms other existing nanopatterning techniques,making it a potential candidate for large-scale carbon nanomanufacturing.