Matteo Scandola — University of Trento #
Analyzing Active Droplet Dynamics: Leveraging Image Processing for Non-Equilibrium Statistical Physics #
Active  matter  can  harness  energy  from  its  surroundings  and  propel  itself  away  from  equilibrium. Its constituents absorb energy from the environment and dissipate it, e.g. through motion or  the  exertion  of  mechanical  forces.  The  investigation  of  these  systems  offers  promising  new perspectives  on  the  field  of  non-equilibrium  statistical  physics,  further  paving  the  way  for  the design of innovative life-like materials and devices.  In this work, we analyse the behaviour of a synthetic active matter system consisting of liquid Ethyl Silicate  droplet  surfers  [1],  whose  self-propulsion  decays  over  time,  within  a  Sodium  Dodecyl Sulfate (SDS) solution [2]. By relying on a synergetic combination of techniques, such as computer vision  algorithms  for  accurate  droplet  detection  [3]  and  analyses  grounded  on  non-equilibrium statistical mechanics and graph theory, we quantitatively characterise all the stages of the dynamic evolution  of  the  system,  from  its  initial  diffusive  regime  up  to  the  generation  of  large  clusters  of droplets that appear as the activity wanes. The presented work showcases a comprehensive analysis of  an  actively  evolving  system,  offering  not  only  a  general  pipeline  for  the  investigation  of analogous  problems,  but  also  a  deep  perspective  at  the  intersection  between  physics  and  synthetic biology.   1]  C.  Watanabe,  S.  Tanaka,  R.  J.  Löffler,  M.  M.  Hanczyc  and  J.  Gorecki,  Dynamic  ordering  caused  by  a source-sink relation between two droplets, Soft Matter, 2022, 18, 6465–6474.  [2] Shinpei, Tanaka et al. “Dynamic Ordering in a Swarm of Floating Droplets Driven by Solutal Marangoni Effect.” Journal of the Physical Society of Japan 86 (2017): 101004.  [3] Martin Weigert, Uwe Schmidt, Robert Haase, Ko Sugawara, and Gene Myers. Star- convex polyhedra for 3d object detection and segmentation in microscopy. In The IEEE Winter Conference on Applications of Computer Vision (WACV), March 2020.