The on-set of ice nucleation in mixed-phase clouds determines cloud lifetime and their microphysical properties. In this work, we develop a novel method that differentiates the early and later transition phases of mixed-phase clouds, i.e., ice crystals are initially surrounded by supercooled liquid water droplets, then as they grow, pure ice segments are formed. Using this method, we examine the relationship between the macrophysical and microphysical properties of mixed-phase clouds. The results show that evolution of cloud macrophysical properties, represented by the increasing spatial ratio of regions containing ice crystals relative to the total in-cloud region (defined as ice spatial ratio), is positively correlated with the evolution of microphysical properties, represented by the increasing ice water content and decreasing liquid water content. The mass partition transition from liquid to ice becomes more significant during the later transition phase (i.e., transition phase 3) when pure ice cloud regions (ICRs) start to appear. Occurrence frequencies of cloud thermodynamic phases show significant transition from liquid to ice at a similar temperature (i.e., -17.5 °C) among three types of definitions of mixed-phase clouds based on ice mass fraction, ice number fraction, or ice spatial ratio. Aerosol indirect effects are quantified for different transition phases using number concentrations of aerosols greater than 100 nm or 500 nm (N>100 and N>500, respectively). N>500 shows stronger positive correlations with ice spatial ratios compared with N>100. This result indicates that larger aerosols potentially contain ice nucleating particles, which facilitate the formation of ice crystals in mixed-phase clouds. The impact of N>500 is also more significant on the earlier transition phase when ice crystals just start to appear compared with the later transition phase. The thermodynamic and dynamic conditions are quantified for each transition phase. The results show in-cloud turbulence as a main mechanism for both the initiation of ice nucleation and the maintenance of supercooled liquid water, while updrafts are important for the latter but not the former. Overall, these results illustrate the varying effects of aerosols, thermodynamics, and dynamics throughout cloud evolution based on this new method that categorizes cloud transition phases.
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A Microfluidic Device for Automated High Throughput Detection of Ice Nucleation of Snomax®
Measurement of ice nucleation (IN) temperature of liquid solutions at sub-ambient temperatures has applications in atmospheric, water quality, food storage, protein crystallography and pharmaceutical sciences. Here we present details on the construction of a temperature-controlled microfluidic platform with multiple individually addressable temperature zones and on-chip temperature sensors for high-throughput IN studies in droplets. We developed, for the first time, automated droplet freezing detection methods in a microfluidic device, using a deep neural network (DNN) and a polarized optical method based on intensity thresholding to classify droplets without manual counting. This platform has potential applications in continuous monitoring of liquid samples consisting of aerosols to quantify their IN behavior, or in checking for contaminants in pure water. A case study of the two detection methods was performed using Snomax® (Snomax International, Englewood, CO, USA), an ideal ice nucleating particle (INP). Effects of aging and heat treatment of Snomax® were studied with Fourier transform infrared (FTIR) spectroscopy and a microfluidic platform to correlate secondary structure change of the IN protein in Snomax® to IN temperature. It was found that aging at room temperature had a mild impact on the ice nucleation ability but heat treatment at 95 °C had a more pronounced effect by reducing the ice nucleation onset temperature by more than 7 °C and flattening the overall frozen fraction curve. Results also demonstrated that our setup can generate droplets at a rate of about 1500/min and requires minimal human intervention for DNN classification.
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- Award ID(s):
- 1801971
- NSF-PAR ID:
- 10232176
- Date Published:
- Journal Name:
- Micromachines
- Volume:
- 12
- Issue:
- 3
- ISSN:
- 2072-666X
- Page Range / eLocation ID:
- 296
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3390/mi12030296
