Research Data Leeds Repository

Data for ‘On-chip density-based sorting of supercooled droplets and frozen droplets in continuous flow’

Citation

Porter, Grace C. E. and Sikora, Sebastien N. F. and Shim, Jung-uk and Murray, Benjamin J. and Tarn, Mark D. (2020) Data for ‘On-chip density-based sorting of supercooled droplets and frozen droplets in continuous flow’. University of Leeds. [Dataset] https://doi.org/10.5518/848

Dataset description

The freezing of supercooled water to ice and the materials which catalyse this process are of fundamental interest to a wide range of fields. At present, our ability to control, predict or monitor ice formation processes is poor. The isolation and characterisation of frozen droplets from supercooled liquid droplets would provide a means of improving our understanding and control of these processes. Here, we have developed a microfluidic platform for the continuous flow separation of frozen from unfrozen picolitre droplets based on differences in their density, thus allowing the sorting of ice crystals and supercooled water droplets into different outlet channels with 94 ± 2% efficiency. This will, in future, facilitate downstream or off-chip processing of the frozen and unfrozen populations, which could include the analysis and characterisation of ice-active materials or the selection of droplets with a particular ice-nucleating activity.

Subjects: F000 - Physical sciences > F300 - Physics > F330 - Environmental physics > F331 - Atmospheric physics
H000 - Engineering > H600 - Electronic & electrical engineering > H650 - Systems engineering
Divisions: Faculty of Engineering and Physical Sciences > School of Physics and Astronomy
Faculty of Environment > School of Earth and Environment
Related resources:
LocationType
https://doi.org/10.1039/D0LC00690DPublication
https://eprints.whiterose.ac.uk/169448/Publication
https://etheses.whiterose.ac.uk/29546/Ethesis
License: Creative Commons Attribution 4.0 International (CC BY 4.0)
Date deposited: 04 Jan 2021 09:19
URI: https://archive.researchdata.leeds.ac.uk/id/eprint/801

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