Ice nucleating particles (INPs) in sea spray aerosols (SSAs) are critical in estimating cloud radiative forcing and precipitation with implications in climate change. Laboratory experiments simulating the ocean-atmosphere environment are becoming increasingly popular for studying the nature of INPs in SSAs. Understanding the ice nucleating characteristics of bulk seawater and the sea surface microlayer (SSML) can provide valuable information about the emitted SSAs. Samples for this study were collected from a waveflume during a phytoplankton bloom and analyzed with complementary methods. The primary method used is a polydimethylsiloxane (PDMS)-based microfluidic static well array, designed and fabricated to trap droplets and measure the ice nucleation (IN) spectra of nanoliter-scale droplets of bulk seawater and SSML. Droplets were subsequently dehydrated in situ until efflorescence, and the dried residual particle morphology was correlated to the droplet IN temperature. Four distinct morphologies were found in the effloresced droplets, among which the aggregate and amorphous morphologies were present in larger amounts in the SSML compared to bulk. These particles also had a different IN spectra, nucleating ice at warmer temperatures than the single and fractal crystal morphologies. The microfluidic studies were complemented by micro-Raman spectroscopy and immersion freezing measurements of larger droplets assessing IN sensitivity to heat and organic carbon removal in an ice spectrometer (IS). This study highlights the compositional diversity of marine samples and paves the way for novel multiplexed microfluidic approaches to study the chemical and biological complexity behind the IN activity of aerosol liquid samples in an integrated platform.

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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