| Abstrakt: |
Aerosol-cloud interactions, including the ice nucleation of supercooled liquid water droplets caused by ice nucleating particles (INPs) and macromolecules (INMs), are a source of uncertainty in predicting future climate. Because of INPs' and INMs'; spatial and temporal heterogeneity in source, number, and composition, predicting their concentration and distribution is a challenge, requiring apt analytical instrumentation. Here, we present the development of our drop Freezing Ice Nucleation Counter (FINC), a droplet freezing technique (DFT), for the quantification of INP and INM concentrations in the immersion freezing mode. FINC's design builds upon previous DFTs and uses an ethanol bath to cool sample aliquots while detecting freezing using a camera. Specifically, FINC uses 288 sample wells of 5-60 µL volume, has a limit of detection of -25.37 ± 0.15 °C with 5 µL, and has an instrument temperature uncertainty of ± 0.5 °C. We further conducted freezing control experiments to quantify the non-homogeneous behavior of our developed DFT, including the consideration of eight different sources of contamination. As part of the validation of FINC, an intercomparison campaign was conducted using an NX-illite suspension and an ambient aerosol sample with two other drop-freezing instruments: ETH's DRoplet Ice Nuclei Counter Zurich (DRINCZ) and University of Basel's LED-based ice nucleation detection apparatus (LINDA). We also tabulated an exhaustive list of peer-reviewed DFTs, to which we added our characterized and validated FINC. In addition, we propose herein the use of a water-soluble biopolymer, lignin, as a suitable ice nucleating standard. An ideal INM standard should be inexpensive, accessible, reproducible, unaffected by sample preparation, and consistent across techniques. First, we show that commercial lignin has a consistent ice nucleating activity across product batches. Second, we demonstrate that aqueous lignin solutions exhibit good solution stability over time. Third, we compare its freezing temperature across different drop- freezing instruments, including on DRINCZ, LINDA, and on the Weizmann Institute's Supercooled Droplets Observation on a Microarray (WISDOM) and determine an empirical fit parameter for future drop freezing validations. With these findings, we aim to show that lignin can be used as a good immersion freezing standard in future technique intercomparisons in the field of atmospheric ice nucleation. [ABSTRACT FROM AUTHOR] |
