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AbstractNatural porous systems, such as soil, membranes, and biological tissues comprise disordered structures characterized by dead-end pores connected to a network of percolating channels. The release and dispersion of particles, solutes, and microorganisms from such features is key for a broad range of environmental and medical applications including soil remediation, filtration and drug delivery. Yet, owing to the stagnant and opaque nature of these disordered systems, the role of microscopic structure and flow on the dispersion of particles and solutes remains poorly understood. Here, we use a microfluidic model system that features a pore structure characterized by distributed dead-ends to determine how particles are transported, retained and dispersed. We observe strong tailing of arrival time distributions at the outlet of the medium characterized by power-law decay with an exponent of 2/3. Using numerical simulations and an analytical model, we link this behavior to particles initially located within dead-end pores, and explain the tailing exponent with a hopping across and rolling along the streamlines of vortices within dead-end pores. We quantify such anomalous dispersal by a stochastic model that predicts the full evolution of arrival times. Our results demonstrate how microscopic flow structures can impact macroscopic particle transport.
Science, Q, Microfluidics, Models, Biological, Article, [PHYS] Physics [physics], Applied physics, Soil, Fluid dynamics, Dispersion in porous media, microfluidics, anomalous transport, dead-end pores, porous media, fluid mechanics, microscopy, Civil engineering, Hydrology, Porosity, Filtration
Science, Q, Microfluidics, Models, Biological, Article, [PHYS] Physics [physics], Applied physics, Soil, Fluid dynamics, Dispersion in porous media, microfluidics, anomalous transport, dead-end pores, porous media, fluid mechanics, microscopy, Civil engineering, Hydrology, Porosity, Filtration
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| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 10% | |
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