Engulfment Avalanches and Thermal Hysteresis for Antifreeze Proteins on Supercooled Ice

Antifreeze proteins (AFPs) bind to the ice-water surface and prevent ice growth at temperatures below 0 °C through a Gibbs-Thomson effect. Each adsorbed AFP creates a metastable depression on the surface that locally resists ice growth, until ice engulfs the AFP. We recently predicted the susceptibility to engulfment as a function of AFP size, distance between AFPs, and supercooling [ J. Chem. Phys. 2023, 158, 094501]. For an ensemble of AFPs adsorbed on the ice surface, the most isolated AFPs are the most susceptible, and when an isolated AFP gets engulfed, its former neighbors become more isolated and more susceptible to engulfment. Thus, an initial engulfment event can trigger an avalanche of subsequent engulfment events, leading to a sudden surge of unrestrained ice growth. This work develops a model to predict the supercooling at which the first engulfment event will occur for an ensemble of randomly distributed AFP pinning sites on an ice surface. Specifically, we formulate an inhomogeneous survival probability that accounts for the AFP coverage, the distribution of AFP neighbor distances, the resulting ensemble of engulfment rates, the ice surface area, and the cooling rate. We use the model to predict thermal hysteresis trends and compare with experimental data.

CNT effective interfacial energy and pre-exponential kinetic factor from measured NaCl crystal nucleation time distributions in contracting microdroplets

Nucleation, the birth of a stable cluster from a disorder, is inherently stochastic. Yet up to date, there are no quantitative studies on NaCl nucleation that accounts for its stochastic nature. Here, we report the first stochastic treatment of NaCl-water nucleation kinetics. Using a recently developed microfluidic system and evaporation model, our measured interfacial energies extracted from a modified Poisson distribution of nucleation time show an excellent agreement with theoretical predictions. Furthermore, analysis of nucleation parameters in 0.5, 1.5, and 5.5 pl microdroplets reveals an interesting interplay between confinement effects and shifting of nucleation mechanisms. Overall, our findings highlight the need to treat nucleation stochastically rather than deterministically to bridge the gap between theory and experiment.

Drying of bio-colloidal sessile droplets: Advances, applications, and perspectives

Drying of biologically-relevant sessile droplets, including passive systems such as DNA, proteins, plasma, and blood, as well as active microbial systems comprising bacterial and algal dispersions, has garnered considerable attention over the last decades. Distinct morphological patterns emerge when bio-colloids undergo evaporative drying, with significant potential in a wide range of biomedical applications, spanning bio-sensing, medical diagnostics, drug delivery, and antimicrobial resistance. Consequently, the prospects of novel and thrifty bio-medical toolkits based on drying bio-colloids have driven tremendous progress in the science of morphological patterns and advanced quantitative image-based analysis. This review presents a comprehensive overview of bio-colloidal droplets drying on solid substrates, focusing on the experimental progress during the last ten years. We provide a summary of the physical and material properties of relevant bio-colloids and link their native composition (constituent particles, solvent, and concentrations) to the patterns emerging due to drying. We specifically examined the drying patterns generated by passive bio-colloids (e.g., DNA, globular, fibrous, composite proteins, plasma, serum, blood, urine, tears, and saliva). This article highlights how the emerging morphological patterns are influenced by the nature of the biological entities and the solvent, micro- and global environmental conditions (temperature and relative humidity), and substrate attributes like wettability. Crucially, correlations between emergent patterns and the initial droplet compositions enable the detection of potential clinical abnormalities when compared with the patterns of drying droplets of healthy control samples, offering a blueprint for the diagnosis of the type and stage of a specific disease (or disorder). Recent experimental investigations of pattern formation in the bio-mimetic and salivary drying droplets in the context of COVID-19 are also presented. We further summarized the role of biologically active agents in the drying process, including bacteria, algae, spermatozoa, and nematodes, and discussed the coupling between self-propulsion and hydrodynamics during the drying process. We wrap up the review by highlighting the role of cross-scale in situ experimental techniques for quantifying sub-micron to micro-scale features and the critical role of cross-disciplinary approaches (e.g., experimental and image processing techniques with machine learning algorithms) to quantify and predict the drying-induced features. We conclude the review with a perspective on the next generation of research and applications based on drying droplets, ultimately enabling innovative solutions and quantitative tools to investigate this exciting interface of physics, biology, data sciences, and machine learning.

Conceptual Validation of Stochastic and Deterministic Methods To Estimate Crystal Nucleation Rates

This work presents a generalized framework to assess the accuracy of methods to estimate primary and secondary nucleation rates from experimental data. The crystallization process of a well-studied model compound was simulated by means of a novel stochastic modeling methodology. Nucleation rates were estimated from the simulated data through multiple methods and were compared with the true values. For primary nucleation, no method considered in this work was able to estimate the rates accurately under general conditions. Two deterministic methods that are widely used in the literature were shown to overpredict rates in the presence of secondary nucleation. This behavior is shared by all methods that extract rates from deterministic process attributes, as they are insensitive to primary nucleation if secondary nucleation is sufficiently fast. Two stochastic methods were found to be accurate independent of whether secondary nucleation is present, but they underestimated rates in the case where a large number of primary nuclei are formed. We hence proposed a criterion to probe the accuracy of stochastic methods for arbitrary data sets, thus providing the theoretical foundations required for their rational use. Finally, we showed how both primary and secondary nucleation rates can be inferred from the same set of detection time data by combining deterministic and stochastic considerations.

Evaporation Dynamics of Sessile Saline Microdroplets in Oil

The occurrence of concentration and temperature gradients in saline microdroplets evaporating directly in air makes them unsuitable for nucleation studies where homogeneous composition is required. This can be addressed by immersing the droplet in oil under regulated humidity and reducing the volume to the picoliter range. However, the evaporation dynamics of such a system is not well understood. In this work, we present evaporation models applicable for arrays of sessile microdroplets with dissolved solute submerged in a thin layer of oil. Our model accounts for the variable diffusion distance due to the presence of the oil film separating the droplet and air, the variation of the solution density and water activity due to the evolving solute concentration, and the diffusive interaction between neighboring droplets. Our model shows excellent agreement with experimental data for both pure water and NaCl solution. With this model, we demonstrate that assuming a constant evaporation rate and neglecting the diffusive interactions can lead to severe inaccuracies in the measurement of droplet concentration, particularly during nucleation experiments. Given the significance of droplet evaporation in a wide array of scientific and industrial applications, the models and insights presented herein would be of great value to many fields of interest.