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When a sessile droplet of a complex fluid dries, a stain forms on the solid surface. The structure and pattern of the stain can be used to detect the presence of a specific chemical compound in the sessile droplet. In the present work, we investigate what parameters of the stain or its formation can be used to characterize the specific interaction between an aqueous dispersion of beads and its receptor immobilized on the surface. We use the biotin-streptavidin system as an experimental model. Clear dissimilarities were observed in the drying sequences on streptavidin-coated substrates of droplets of aqueous solutions containing biotin-coated or streptavidin-coated beads. Fluorescent beads are used in order to visualize the fluid flow field. We show differences in the distribution of the particles on the surface depending on biomolecular interactions between beads and the solid surface. A mechanistic model is proposed to explain the different patterns obtained during drying. The model describes that the beads are left behind the receding wetting line rather than pulled towards the drop center if the biological binding force is comparable to the surface tension of the receding wetting line. Other forces such as the viscous drag, van der Waals forces, and solid-solid friction forces are found negligible. Simple microfluidics experiments are performed to further illustrate the difference in behavior where is adhesion or friction are present between the bead and substrate due to the biological force. The results of the model are in agreement with the experimental observations which provide insight and design capabilities. A better understanding of the effects of the droplet-surface interaction on the drying mechanism is a crucial first step before the identification of drying patterns can be promisingly applied to areas such as immunology and biomarker detection. Comments NOTICE: this is the author's version of a work that was accepted for publication in Chemical Engineering Science. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Chemical Engineering Science, [137, (2015)

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Droplet evaporation plays crucial roles in biodiagnostics, microfabrication, and inkjet printing. Experimentally studying the evolution of a sessile droplet consisting of two or more components needs sophisticated equipment to control the vast parameter space affecting the physical process. On the other hand, the non-axisymmetric nature of the problem, attributed to compositional perturbations, introduces challenges to numerical methods. In this work, droplet evaporation problem is studied from a new perspective. We analyze a sessile methanol droplet evolution through data-driven classification and regression techniques. The models are trained using experimental data of methanol droplet evolution under various environmental humidity levels and substrate temperatures. At higher humidity levels, the interfacial tension and subsequently contact angle increase due to higher water uptake into droplet. Therefore, different regimes of evolution are observed due to adsorption–absorption and possible condensation of water which turns the droplet from a single component into a binary system. In this work, machine learning and data-driven techniques are utilized to estimate the regime of droplet evaporation, the time evolution of droplet base diameter and contact angle, and level of surrounding humidity. Droplet regime is estimated by classification algorithms through point-by-point analysis of droplet profile. Decision tree demonstrates a better performance compared to Naïve Bayes (NB) classifier. Additionally, the level of surrounding humidity, as well as the time evolution of droplet base diameter and contact angle, are estimated by regression algorithms. The estimation results show promising performance for four cases of methanol droplet evolution under conditions unseen by the model, demonstrating the model’s capability to capture the complex physics underlying binary droplet evolution.