The investigation of the transport processes at the interface of liquid and air is important for different chemical and biological applications. Therefore, it is important to understand all transport processes in microfluidic systems which provide great motivations to answer several important scientific questions and help discover new applications. One of the successful microfluidic systems is free-surface microfluidics capable of absorbing airborne molecules in a shallow free-surface microchannel for detection of hazardous gases using Surface Enhanced raman Spectroscopy (SERS). However, full understanding of all transport processes is very complicated in this system. Therefore, we focused on the transport processes such as evaporation, stirring process, and aggregation kinetics in sessile droplets with pinned contact-line. We developed a unified numerical model to find the evaporation rate and internal flows in a sessile droplet with pinned contact-line.

The temperature gradient on the interface causes an internal flow due to Marangoni stress, which provides stirring effect. Simultaneously, as the droplet height decreases, the evaporation-induced flow creates a jet-like flow radially towards the contact-line which is responsible for formation of `coffee-ring stain' after evaporation. In addition, we reported a simple polynomial correlation for dimensionless evaporation time as a function of initial contact angle. Finally, the aggregation kinetics of silver nanoparticles in sessile droplets was investigated in order to determine the conditions for optimal SERS detection. The aggregation reaction ceases when the solvent evaporates forming a stain consisting of a high concentration of aggregates which can be interrogated with spectrometer for analyte detection.

For a well stirred droplet, the optimal condition for SERS detection was found to be at kcNPtevap = 0.3, which is a product of the dimerization rate constant (k), the concentration of nanoparticles (cNP), and the droplet evaporation time (tevap). Near maximal signal is observed which defines a time window during which trace analytes can be measured. The results of the simulations revealed a very good agreement with experimentally acquired SERS spectra using gas-phase 1,4-BDT as a model analyte. Finally, the detection of Nicotine from breath was performed to prove the ability of this platform for the breath diagnostics.