Francois Peaudecerf

Random network peristalsis in Physarum polycephalum organizes fluid flows across an individual

Individuals can function as integrated organisms only when information and resources are shared across a body. Signals and substrates are commonly moved using fluids, often channeled through a network of tubes. Peristalsis is one mechanism for fluid transport and is caused by a wave of cross-sectional contractions along a tube. We extend the concept of peristalsis from the canonical case of one tube to a random network. Transport is maximized within the network when the wavelength of the peristaltic wave is of the order of the size of the network. The slime mold Physarum polycephalum grows as a random network of tubes, and our experiments confirm peristalsis is used by the slime mold to drive internal cytoplasmic flows. Comparisons of theoretically generated contraction patterns with the patterns exhibited by individuals of P. polycephalum demonstrate that individuals maximize internal flows by adapting patterns of contraction to size, thus optimizing transport throughout an organism. This control of fluid flow may be the key to coordinating growth and behavior, including the dynamic changes in network architecture seen over time in an individual.

The hummingbird's tongue: a self-assembling capillary syphon

We present the results of a combined experimental and theoretical investigation of the dynamics of drinking in ruby-throated hummingbirds. In vivo observations reveal elastocapillary deformation of the hummingbirds tongue and capillary suction along its length. By developing a theoretical model for the hummingbirds drinking process, we investigate how the elastocapillarity affects the energy intake rate of the bird and how its open tongue geometry reduces resistance to nectar uptake. We note that the tongue flexibility is beneficial for accessing, transporting and unloading the nectar. We demonstrate that the hummingbird can attain the fastest nectar uptake when its tongue is roughly semicircular. Finally, we assess the relative importance of capillary suction and a recently proposed fluid trapping mechanism, and conclude that the former is important in many natural settings.

On a tweezer for droplets.

We describe the physics behind a peculiar feeding mechanism of a certain class of shorebirds, in which they transport their prey in droplets from their beak tips mouthwards. The subtle interplay between the drop and the beaks tweezering motion allows the birds to defy gravity through driving the drop upwards. This mechanism provides a novel example of dynamic boundary-driven drop motion, and suggests how to design tweezers for drops, able to trap and to move small amounts of liquid.

Traces of surfactants can severely limit the drag reduction of superhydrophobic surfaces

Significance Whereas superhydrophobic surfaces (SHSs) have long promised large drag reductions, experiments have provided inconsistent results, with many textures yielding little or no benefit. Given the vast potential impact of SHSs on energy utilization, finding an explanation and mitigating strategies is crucially important. A recent hypothesis suggests surfactant-induced Marangoni stresses may be to blame. However, paradoxically, adding surfactants has a barely measurable effect, casting doubt on this hypothesis. By performing surfactant-laden simulations and unsteady experiments we demonstrate the impact of surfactants and how extremely low concentrations, unavoidable in practice, can increase drag up to complete immobilization of the air–liquid interface. Our approach can be used to test other SHS textures for sensitivity to surfactant-induced stresses. Superhydrophobic surfaces (SHSs) have the potential to achieve large drag reduction for internal and external flow applications. However, experiments have shown inconsistent results, with many studies reporting significantly reduced performance. Recently, it has been proposed that surfactants, ubiquitous in flow applications, could be responsible by creating adverse Marangoni stresses. However, testing this hypothesis is challenging. Careful experiments with purified water already show large interfacial stresses and, paradoxically, adding surfactants yields barely measurable drag increases. To test the surfactant hypothesis while controlling surfactant concentrations with precision higher than can be achieved experimentally, we perform simulations inclusive of surfactant kinetics. These reveal that surfactant-induced stresses are significant at extremely low concentrations, potentially yielding a no-slip boundary condition on the air–water interface (the “plastron”) for surfactant concentrations below typical environmental values. These stresses decrease as the stream-wise distance between plastron stagnation points increases. We perform microchannel experiments with SHSs consisting of stream-wise parallel gratings, which confirm this numerical prediction, while showing near-plastron velocities significantly slower than standard surfactant-free predictions. In addition, we introduce an unsteady test of surfactant effects. When we rapidly remove the driving pressure following a loading phase, a backflow develops at the plastron, which can only be explained by surfactant gradients formed in the loading phase. This demonstrates the significance of surfactants in deteriorating drag reduction and thus the importance of including surfactant stresses in SHS models. Our time-dependent protocol can assess the impact of surfactants in SHS testing and guide future mitigating designs.

Microbial mutualism at a distance: The role of geometry in diffusive exchanges

Gates Cambridge Trust The Winton Foundation for the Physics of Sustainability The Royal Society The Schlumberger Chair Fund

Microbial mutualism at a distance

Complex microbial communities play essential roles in the proper functioning of the environment, in maintaining the health of plants and animals, and in many industrial processes. Within these communities, microbial interactions are often predicated on metabolism, as auxotrophs depend on nutrients made by other microbes. Here, we investigate a mathematical model of growth and interactions between mutualistic microbial populations separated in space but coupled by a channel through which nutrients are exchanged diffusively. The model is used to study mutualistic algal-bacterial interactions, focusing on a synthetic model system. Solutions to the model reveal rich dynamics, and allow prediction of the conditions for the successful establishment of remote mutualisms. We connect our findings to understanding complex behaviour in synthetic and naturally occurring microbial communities.The exchange of diffusive metabolites is known to control the spatial patterns formed by microbial populations, as revealed by recent studies in the laboratory. However, the matrices used, such as agarose pads, lack the structured geometry of many natural microbial habitats, including in the soil or on the surfaces of plants or animals. Here we address the important question of how such geometry may control diffusive exchanges and microbial interaction. We model mathematically mutualistic interactions within a minimal unit of structure: two growing reservoirs linked by a diffusive channel through which metabolites are exchanged. The model is applied to study a synthetic mutualism, experimentally parameterised on a model algal-bacterial co-culture. Analytical and numerical solutions of the model predict conditions for the successful establishment of remote mutualisms, and how this depends, often counterintutively, on diffusion geometry. We connect our findings to understanding complex behaviour in synthetic and naturally occurring microbial communities.

Video: Soap opera in the maze: Geometry matters in Marangoni flows

Fernando Temprano-Coleto,1,* François J. Peaudecerf,2 Julien R. Landel,3 Frédéric Gibou,1,4 and Paolo Luzzatto-Fegiz1 1Department of Mechanical Engineering, University of California Santa Barbara, Santa Barbara, California 93106, United States 2Department of Civil, Environmental, and Geomatic Engineering, ETH Zürich, 8093 Zürich, Switzerland 3School of Mathematics, Alan Turing Building, University of Manchester, Oxford Road, Manchester M13 9PL, United Kingdom 4Department of Computer Science, University of California Santa Barbara, Santa Barbara, California 93106, United States