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researcher

J. Burton

26 papers hereh-index 201.6k citations117 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author4
  • middle author6
  • last author16

Across the 26 of 26 papers where every author was matched, so the position is known.

fields
  • cond-mat.soft13
  • physics.flu-dyn9
  • nlin.AO2
  • physics.geo-ph1
  • physics.plasm-ph1
same name
  • J. Burton — 17 papers, h 30
  • J. Burton — 4 papers, h 3
  • J. Burton — 3 papers, h 4
  • J. Burton — 2 papers, h 1
  • J. Burton — 2 papers, h 2
  • J. Burton — 2 papers, h 3

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20102023
most citedCoalescence of bubbles and drops in an outer fluid

251 citations · 1.2k across the 26 of their papers we have counts for

collaborators
Showing 2022Show all

4 papers · 1 filter

cond-mat.soft2022★ 8 cited

Coalescing Clusters Unveil New Regimes of Frictional Fluid Mechanics

Haicen Yue, Justin C. Burton, Daniel M. Sussman

Droplet coalescence is essential in a host of biological and industrial processes, involving complex systems as diverse as cellular aggregates, colloidal suspensions, and polymeric…

physics.geo-ph2022

The lifetime of charged dust in the atmosphere

Josh Méndez Harper, Dana Harvey, Tianshu Huang +3

Windblown dust plays critical roles in numerous geophysical and biological systems, yet current models fail to explain the transport of coarse-mode particles (>5 μm) to great dis…

cond-mat.soft2022★ 4 cited

Dynamics of Mass Polar Spheroids During Sedimentation

Kavinda J. Nissanka, Xiaolei Ma, Justin C. Burton

The dynamics of sedimenting particles under gravity are surprisingly complex due to the presence of effective long-ranged forces. When the particles are polar with a well-defined s…

cond-mat.soft2022★ 19 cited

Extracting Forces from Noisy Dynamics in Dusty Plasmas

Wentao Yu, Jonathan Cho, Justin C. Burton

Extracting environmental forces from noisy data is a common yet challenging task in complex physical systems. Machine learning represents a robust approach to this problem, yet is…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.