Repository logo
  • English
  • العربية
  • বাংলা
  • Català
  • Čeština
  • Deutsch
  • Ελληνικά
  • Español
  • Suomi
  • Français
  • Gàidhlig
  • हिंदी
  • Magyar
  • Italiano
  • Қазақ
  • Latviešu
  • Nederlands
  • Polski
  • Português
  • Português do Brasil
  • Srpski (lat)
  • Српски
  • Svenska
  • Türkçe
  • Yкраї́нська
  • Tiếng Việt
Log In
New user? Click here to register.Have you forgotten your password?
  1. Home
  2. Scholalry Output
  3. Publications
  4. Shear-induced dynamics of an active Belousov-Zhabotinsky droplet
 
  • Details

Shear-induced dynamics of an active Belousov-Zhabotinsky droplet

Source
Soft Matter
ISSN
1744683X
Date Issued
2025-02-19
Author(s)
Shenoy, Shreyas A.
Chaithanya, K. V.S.
Dayal, Pratyush  
DOI
10.1039/d4sm01464b
Volume
21
Issue
10
Abstract
Controlled navigation of self-propelled active matter in complex biological environments has remained a significant challenge in engineering owing to a multitude of interactions that persist in the process. Active droplets, being some of the several synthetic active matters, have garnered significant attention owing to their ability to exhibit dynamic shape changes, self-sustained motion, interact with external stimuli such as flows, and mimic biological active matter. Here, we explore the dynamics of a self-propelled active droplet powered by the oscillatory Belousov-Zhabotinsky (BZ) reaction in the presence of a shear flow. We adapt a multicomponent lattice Boltzmann method (LBM) in conjunction with the phase-field model to simulate the droplet's interaction with the surrounding fluid. We unravel the collective effect of droplet deformation, reaction kinetics, and strength of the surrounding shear flow on droplet dynamics. Our findings depict that the shear flow disrupts the initial isotropic surface tension, and produces concentration nucleation spots in the droplet. The asymmetry thus generated produces Marangoni flow that ultimately propels the droplet. Our findings provide valuable insights into the mechanisms governing active droplet behavior and open new avenues for designing controllable synthetic active matter systems with potential applications in microfluidics, targeted delivery, and biomimetic technologies. In addition, our framework can potentially be integrated with the physics-informed machine learning framework to develop more efficient mesh-free methods.
Publication link
https://doi.org/10.1039/d4sm01464b
URI
https://d8.irins.org/handle/IITG2025/28251
IITGN Knowledge Repository Developed and Managed by Library

Built with DSpace-CRIS software - Extension maintained and optimized by 4Science

  • Privacy policy
  • End User Agreement
  • Send Feedback
Repository logo COAR Notify