The surface temperature can significantly affect the flow field of drying droplets. Most previous studies assumed a monotonic temperature variation along the droplet surface. However, the present analyses indicate that a nonmonotonic spatial distribution of the surface temperature should occur. Three different patterns of the surface temperature distribution may appear during the evaporation process of liquid droplets: (i) the surface temperature increases monotonically from the center to the edge of the droplet; (ii) the surface temperature exhibits a nonmonotonic spatial distribution along the droplet surface; (iii) the surface temperature decreases monotonically from the center to the edge of the droplet. These surface temperature distributions can be explained by combining the evaporative cooling at the droplet surface and the heat conduction across the substrate and the liquid. Furthermore, a "phase diagram" for the distribution of the surface temperature is introduced and the effect of the spatial temperature distribution along the droplet surface on the flow structure of the droplet is discussed. The results may provide a better understanding of the Marangoni effect of drying droplets and provide a potential way to control evaporation-driven deposition as well as the assembly of colloids and other materials.
Summary
Surface wave retrieval from ambient noise records using seismic interferometry techniques have been widely used for multiscale shear wave velocity (Vs) imaging. One key step during Vs imaging is the generation of dispersion spectra and the extraction of a reliable dispersion curve from the retrieved surface waves. However, the sparse array geometry usually affects the ability for high-frequency (>1 Hz) seismic signals acquisition. Dispersion measurements are degraded by array response due to sparse sampling and often present smeared dispersion spectra with sidelobe artifacts. Previous studies usually focus on interferograms domain (e.g., cross-correlation function) and attempt to enhance coherent signals before dispersion measurement. We propose an alternative technique to explicitly deblur dispersion spectra through use of a phase-weighted slant-stacking algorithm. Numerical examples demonstrate the strength of the proposed technique to attenuate array responses as well as incoherent noise. Three different field examples prove the flexibility and superiority of the proposed technique: the first dataset consists of ambient noise records acquired using a nodal seismometer array; the second dataset utilizes Distributed Acoustic Sensing (DAS) and a marine fiber-optic cable to acquire a similar ambient noise dataset; the last dataset is a vibrator-based active-source surface wave data. The enhanced dispersion measurements provide cleaner and higher-resolution spectra without distortions which will assist both human interpreters as well as ML algorithms in efficiently picking curves for subsequent Vs inversion.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.