Preprints
https://doi.org/10.26434/chemrxiv-2022-ddzv8
https://doi.org/10.26434/chemrxiv-2022-ddzv8
 
28 Nov 2022
28 Nov 2022

HUB: A method to model and extract the distribution of ice nucleation temperatures from drop-freezing experiments

Ingrid de Almeida Ribeiro1, Konrad Meister2,3,4, and Valeria Molinero1 Ingrid de Almeida Ribeiro et al.
  • 1Department of Chemistry, The University of Utah, 315 South 1400 East, Salt Lake City, Utah 84112-0850, USA
  • 2Max Planck Institute for Polymer Research, 55128 Mainz, Germany
  • 3Department of Chemistry and Biochemistry, Boise State University, Boise, Idaho 83725, USA
  • 4Biomolecular Sciences Graduate Programs, Boise State University, Boise, ID 83725, USA

Abstract. The heterogeneous nucleation of ice is an important atmospheric process facilitated by a wide range of aerosols. Drop-freezing experiments are key for the determination of the ice nucleation activity of biotic and abiotic ice nucleators (INs). The results of these experiments are reported as the fraction of frozen droplets fice (T) as a function of decreasing temperature, and the corresponding cumulative freezing spectra Nm (T) computed using Vali’s methodology. The differential freezing spectrum nm (T) is in principle a direct measure of the underlying distribution of heterogeneous ice nucleation temperatures Pu (T) in the sample. However, Nm (T) can be noisy, resulting in a differential form nm (T) that is challenging to interpret. Furthermore, there is no rigorous statistical analysis of how many droplets and dilutions are needed to obtain a well-converged nm (T) that represents the underlying distribution Pu (T). Here, we present the “Heterogeneous Underlying-based” (HUB) method and associated Python codes that model (HUB-forward code) and interpret (HUB-backward code) the results of drop-freezing experiments. HUB-forward is the first available code that predicts fice (T) and Nm (T) from a proposed distribution Pu (T) of IN temperatures, allowing its users to test hypotheses regarding the role of subpopulations of nuclei in freezing spectra, and providing a guide for a more efficient collection of freezing data. HUB-backward uses a stochastic optimization method to compute nm (T) from either Nm (T) or fice (T). The differential spectrum computed with HUB-backward is an analytical function that can be used to reveal and characterize the underlying number of IN subpopulations of complex biological samples (ice nucleating bacteria, fungi and pollen), and quantify the dependence of their subpopulations on environmental variables. By delivering a way to compute the differential spectrum from drop freezing data, and vice-versa, the HUB-forward and HUB-backward codes provide a hub between experiments and interpretative physical quantities that can be analysed with kinetic models and nucleation theory.

Ingrid de Almeida Ribeiro et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'RC: Reviewer comments on egusphere-2022-1242', Nadine Borduas-Dedekind, 13 Jan 2023
  • RC2: 'Comment on egusphere-2022-1242', Anonymous Referee #2, 16 Jan 2023

Ingrid de Almeida Ribeiro et al.

Model code and software

HUB Ingrid de Almeida Ribeiro https://github.com/Molinero-Group/underlying-distribution

Ingrid de Almeida Ribeiro et al.

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Short summary
Ice formation is a key atmospheric process facilitated by a wide range of aerosols. We present a method to model and interpret of ice nucleation experiments and extract the distribution of potency of nucleation sites. We use the method to optimize conditions of laboratory sampling and extract distribution of ice nucleation temperatures from bacteria, fungi and pollen. These reveal unforeseen subpopulations of nuclei in these systems and how they respond to changes in their environment.