Mixing and spreading of di erent liquids are omnipresent in nature, life and technology, such as oil pollution on the sea 1,2 , estuaries 3 , food processing 4 , cosmetic and beverage industries 5,6 , lab-on-a-chip devices 7 , and polymer processing 8 . However, the mixing and spreading mechanisms for miscible liquids remain poorly characterized. Here, we show that a fully soluble liquid drop deposited on a liquid surface remains as a static lens without immediately spreading and mixing, and simultaneously a Marangoni-driven convective flow is generated, which are counterintuitive results when two liquids have di erent surface tensions. To understand the dynamics, we develop a theoretical model to predict the finite spreading time and length scales, the Marangoni-driven convection flow speed, and the finite timescale to establish the quasi-steady state for the Marangoni flow. The fundamental understanding of this solutal Marangoni flow may enable driving bulk flows and constructing an e ective drug delivery and surface cleaning approach without causing surface contamination by immiscible chemical species.When a sessile oil drop is released on top of a water surface, it spreads until a monolayer is achieved 9 , because the liquids are immiscible, as shown in Fig. 1a. In contrast, if a water drop is placed on a water surface, it shows a cascade of coalescence events and the liquids are rapidly mixed (Fig. 1b) 10 . In contrast with these two configurations, we captured unexpected mixing and spreading features between fully miscible liquids. When a drop of alcoholfor example, isopropanol (IPA)-is placed on a water surface, it spontaneously generates a Marangoni convective flow along the outward radial direction and we observed that there is a static liquid lens in the middle ( Fig. 1c and Supplementary Fig. 1), even though these two liquids are infinitely miscible. Here, we discuss solutal Marangoni effects in fully miscible liquids to explain the finite size lens and the associated flow (more details are provided in Supplementary Videos 1-3).To visualize the spreading and mixing pattern of a miscible liquid drop, IPA (volume V = 7.2 ± 0.2 µl), placed on a water bath (400 ml deionized (DI) water in an 196-mm-diameter Petri dish with depth H = 14 mm), we used time-resolved particle tracking velocimetry (PTV) and high-speed schlieren measurement techniques (Supplementary Information). For PTV experiments, we seeded polystyrene particles (diameter = 100 µm) in solution and recorded the particle motion from top and side views. IPA is less dense than water and therefore the sessile drop floats on the surface. The drop initially spreads out and quickly achieves a static central lens with a near constant diameter 2R (see Fig. 1c and Supplementary Figs 5-7) during which the IPA continuously leaks at the boundary (Fig. 1c, Supplementary Fig. 1 and Supplementary Videos 3 and 4). The inset of Fig. 2a shows a top view of a schlieren pattern, that is, an interfacial turbulence structure representing the mass transfer between the...

The accumulation of motile cells at solid interfaces increases the rate of surface encounters and the likelihood of surface attachment, leading to surface colonization and biofilm formation. The cell density distribution in the vicinity of a physical boundary is influenced by the interactions between the microswimmers and their physical environment, including hydrodynamic and steric interactions, as well as by stochastic effects. Disentangling the contributions of these effects remains an experimental challenge. Here, we use a custom-made four-camera view microscope to track a population of motile puller-type Chlamydomonas reinhardtii in a relatively unconstrained three-dimensional (3D) domain. Our experiments yield an extensive sample of 3D trajectories including cell-surface encounters with a planar wall, from which we extract a full description of the dynamics and the stochasticity of swimming cells. We use this large data sample and combine it with Monte Carlo simulations to determine the link between the dynamics at the single-cell level and the cell density. Our experiments demonstrate that the near-wall scattering is bimodal, corresponding to steric and hydrodynamic effects. We find, however, that this near-wall dynamics has little influence on the cell accumulation at the surface. On the other hand, we present evidence of a cell-induced surface-directed rotation leading to a vertical orbiting behavior and hopping trajectories, consistent with long-range hydrodynamic effects. We identify this long-range effect to be at the origin of the significant surface accumulation of cells.

Obtaining accurate experimental data from Lagrangian tracking and tomographic velocimetry requires an accurate camera calibration consistent over multiple views. Established calibration procedures are often challenging to implement when the length scale of the measurement volume exceeds that of a typical laboratory experiment. Here, we combine tools developed in computer vision and non-linear camera mappings used in experimental fluid mechanics, to successfully calibrate a fourcamera setup that is imaging inside a large tank of dimensions ∼ 10 × 25 × 6 m 3. The calibration procedure uses a planar checkerboard that is arbitrarily positioned at unknown locations and orientations. The method can be applied to any number of cameras. The parameters of the calibration yields direct estimates of the positions and orientations of the four cameras as well as the focal lengths of the lenses. These parameters are used to assess the quality of the calibration. The calibration allows us to perform accurate and consistent linear ray-tracing, which we use to triangulate and track fish inside the large tank. An open-source implementation of the calibration in Matlab is available.