Diffusiophoresis-Enhanced Turing Patterns

Abstract

Turing patterns are fundamental in biophysics, emerging from short-range activation and long-range inhibition processes. However, their paradigm is based on diffusive transport processes, which yields Turing patters that are less sharp than the ones observed in nature. A complete physical description of why the Turing patterns observed in nature are significantly sharper than state-of-the-art models remains unknown. Here, we propose a novel solution to this phenomenon by investigating the role of diffusiophoresis in Turing patterns. The inclusion of diffusiophoresis enables one to generate patterns of colloidal particles with significantly finer length scales than the accompanying chemical patterns. Further, diffusiophoresis enables a robust degree of control that closely mimics natural patterns observed in species like the Ornate Boxfish and the Jewel Moray Eel. We present a scaling analysis indicating that chromatophores, ubiquitous in biological pattern formation, are likely diffusiophoretic, and that colloidal Péclet number controls the pattern enhancement. This discovery suggests important features of biological pattern formation can be explained with a universal mechanism that is quantified straightforwardly from the fundamental physics of colloids and inspires future exploration of adaptive materials, lab-on-a-chip devices, and tumorigenesis.

11 pages, 6 figures, 4 supplementary videos

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Cited by in corpus (6)

• Turing pattern theory on homogeneous and heterogeneous higher-order temporal network system
• Reaction-driven Diffusiophoresis of Liquid Condensates: Mechanisms for Intra-cellular Organization
• Solute-mediated colloidal vortex in a microfluidic T-junction
• Unraveling biochemical spatial patterns: machine learning approaches to the inverse problem of Turing patterns
• Convective Instability Driven by Diffusiophoresis of Colloids in Binary Liquid Mixtures
• Imperfect Turing Patterns: Diffusiophoretic Assembly of Hard Spheres via Reaction-Diffusion Instabilities