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Abstract Adaptive and bioinspired droplet-based materials are built using the droplet interface bilayer (DIB) technique, assembling networks of lipid membranes through adhered microdroplets. The properties of these lipid membranes are linked to the properties of the droplets forming the interface. Consequently, rearranging the relative positions of the droplets within the network will also alter the properties of the lipid membranes formed between them, modifying the transmembrane exchanges between neighboring compartments. In this work, we achieved this through the use of magnetic fluids or ferrofluids selectively dispersed within the droplet-phase of DIB structures. First, the ferrofluid DIB properties are optimized for reconfiguration using a coupled experimental-computational approach, exploring the ideal parameters for droplet manipulation through magnetic fields. Next, these findings are applied towards larger, magnetically-heterogeneous collections of DIBs to investigate magnetically-driven reconfiguration events. Activating electromagnets bordering the DIB networks generates rearrangement events by separating and reforming the interfacial membranes bordering the dispersed magnetic compartments. These findings enable the production of dynamic droplet networks capable of modifying their underlying membranous architecture through magnetic forces.
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DropletMask: Leveraging visual data for droplet impact analysis
Abstract Machine learning‐assisted computer vision represents a state‐of‐the‐art technique for extracting meaningful features from visual data autonomously. This approach facilitates 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 convolutional 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.
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- Award ID(s):
- 2045322
- PAR ID:
- 10539532
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Droplet
- Volume:
- 3
- Issue:
- 4
- ISSN:
- 2769-2159
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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