Hierarchical Exploration of Drying Patterns Formed in Drops Containing Lysozyme, PBS, and Liquid Crystals

Texture identification in liquid crystal-protein droplets using evaporative drying, generalized additive modeling, and K-means Clustering

Sessile drying droplets manifest distinct morphological patterns, encompassing diverse systems, viz., DNA, proteins, blood, and protein-liquid crystal (LC) complexes. This study employs an integrated methodology that combines drying droplet, image texture analysis (features from First Order Statistics, Gray Level Co-occurrence Matrix, Gray Level Run Length Matrix, Gray Level Size Zone Matrix, and Gray Level Dependence Matrix), and statistical data analysis (Generalized Additive Modeling and K-means clustering). It provides a comprehensive qualitative and quantitative exploration by examining LC-protein droplets at varying initial phosphate buffered concentrations (0x, 0.25x, 0.5x, 0.75x, and 1x) during the drying process under optical microscopy with crossed polarizing configuration. Notably, it unveils distinct LC-protein textures across three drying stages: initial, middle, and final. The Generalized Additive Modeling (GAM) reveals that all the features significantly contribute to differentiating LC-protein droplets. Integrating the K-means clustering method with GAM analysis elucidates how textures evolve through the three drying stages compared to the entire drying process. Notably, the final drying stage stands out with well-defined, non-overlapping clusters, supporting the visual observations of unique LC textures. Furthermore, this paper contributes valuable insights, showcasing the efficacy of drying droplets as a rapid and straightforward tool for characterizing and classifying dynamic LC textures.

Heat Transfer by Sweat Droplet Evaporation

Sweating regulates the body temperature in extreme environments or during exercise. Here, we investigate the evaporative heat transfer of a sweat droplet at the microscale to unveil how the evaporation complexity of a sweat droplet would affect the body's ability to cool under specific environmental conditions. Our findings reveal that, depending on the relative humidity and temperature levels, sweat droplets experience imperfect evaporation dynamics, whereas water droplets evaporate perfectly at equivalent ambient conditions. At low humidity, the sweat droplet fully evaporates and leaves a solid deposit, while at high humidity, the droplet never reaches a solid deposit and maintains a liquid phase residue for both low and high temperatures. This unprecedented evaporation mechanism of a sweat droplet is attributed to the intricate physicochemical properties of sweat as a biofluid. We suppose that the sweat residue deposited on the surface by evaporation is continuously absorbing the surrounding moisture. This route leads to reduced evaporative heat transfer, increased heat index, and potential impairment of the body's thermoregulation capacity. The insights into the evaporative heat transfer dynamics at the microscale would help us to improve the knowledge of the body's natural cooling mechanism with practical applications in healthcare, materials science, and sports science.

Coupling effects of human serum albumin and sodium chloride on biological desiccation patterns

Desiccation patterns of plasma sessile drops have attracted increasing attention, not only because of the fantastic underlying physics, but also due to their potential of being health diagnostic tools. However, plasma is a multicomponent system, which contains macromolecular proteins and inorganic salts; these components have complicated interactions to define pattern morphologies. Unfortunately, mechanisms of coupling effects of main components on pattern morphologies are still not clear, thus limiting their diagnostic applications. Here we show the coupling effects of human serum albumin (HSA) and sodium chloride (NaCl) on plasma desiccation patterns. Our experiments indicate that NaCl enhances the "coffee ring" effect of HSA to promote its aggregation at the peripheral region and narrows down its aggregation area; this would influence the distribution of internal stresses, resulting in a larger number of radial cracks, with a larger width but a shorter length, than cracks in pure HSA. In the meantime, HSA experiences the gelation process that propagates from the peripheral region to central region and causes the spatiotemporal deviation in the degree of solidification, which induces a higher concentration of NaCl in the central region, thus leading to the formation of crystal patterns. Our further experiments demonstrate that these characteristic patterns are correlated to the variation in the concentration of NaCl, which can be caused by hyponatremia and hypernatremia in real biofluids. Our findings not only provide a new mechanistic insight into biological desiccation patterns, but also bridge the gap between the understanding and diagnostic applications of these desiccation patterns.

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.

Deep learning applied to analyze patterns from evaporated droplets of Viscum album extracts

This paper introduces a deep learning based methodology for analyzing the self-assembled, fractal-like structures formed in evaporated droplets. To this end, an extensive image database of such structures of the plant extract Viscum album Quercus\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$10^{-3}$$\end{document}10-3 was used, prepared by three different mixing procedures (turbulent, laminar, and diffusion based). The proposed pattern analysis approach is based on two stages: (1) automatic selection of patches that exhibit rich texture along the database; and (2) clustering of patches in accordance with prevalent texture by means of a Dense Convolutional Neural Network. The fractality of the patterns in each cluster is verified through Local Connected Fractal Dimension histograms. Experiments with Gray-Level Co-Occurrence matrices are performed to determine the benefit of the proposed approach in comparison with well established image analysis techniques. For the investigated plant extract, significant differences were found between the production modalities; whereas the patterns obtained by laminar flow showed the highest fractal structure, the patterns obtained by the application of turbulent mixture exhibited the lowest fractality. Our approach is the first to analyze, at the pure image level, the clustering properties of regions of interest within a database of evaporated droplets. This allows a greater description and differentiation of the patterns formed through different mixing procedures.