arXiv subjects

Justin C. Burton

Publications and source records attributed to Justin C. Burton.

At least 19 recordsLinked to original sources

arXiv2026

Droplet coalescence in fluids obeying Darcy's law

During drop coalescence, a connecting bridge of fluid forms and rapidly expands due to surface tension. For spherical drops, these dynamics are well understood in both the viscous and inertial regimes. However, under strong confinement, fluid motion is fundamentally altered by geometric constraints, leading to dissipation on small lengthscales. We investigate the coalescence of drops confined in a Hele-Shaw cell (two parallel plates separated by a narrow gap). In this geometry, the depth-averaged flow is governed by Darcy's law while surface tension drives the interface motion. We identify two distinct temporal regimes in the evolution of the bridge radius that evolves as a power law ($R_b$). At early times, the bridge grows as $R_b \sim t^{1/2}$, which results from a confinement-dependent meniscus instability that determines the initiation of contact between droplets prior to bridge formation. At later times, the bridge growth slows substantially and follows $R_b \sim t^{1/5}$, consistent with recent theoretical predictions for Darcy-governed coalescence. We show that the transition between these regimes is controlled by several geometric lengthscales. In particular, the onset of the Darcy regime occurs when the interface radius of curvature becomes comparable to the plate spacing, such that the flow becomes fully confined. Using a boundary integral formulation, we find that both scaling laws for $R_b$ are determined by the bridge width. Together, these results identify a new universal regime of drop coalescence in a broad class of fluids obeying Darcy's law.

physics.flu-dyn↗

arXiv2026

Nonreciprocal interactions drive intermittent melting of Coulomb clusters

Complex systems out of equilibrium often experience intermittent oscillations between quiescent and highly dynamic states. While intermittency is usually driven by stochastic noise or external forcing, energy can also be sourced from field-mediated interactions between particles, which are often nonreciprocal and effectively violate Newton's 3rd law. Here we demonstrate how nonreciprocal interactions produce intermittency in clusters of charged micron-sized particles confined in a plasma sheath. Using three-dimensional particle tracking, we observe that vertical oscillations, induced by a noisy plasma environment, are parametrically coupled to the horizontal vibrational modes. Experiments and simulations show that nonreciprocal interactions strongly amplify this coupling, injecting energy into the system. This mechanism triggers explosive melting transitions from an ordered cluster to an ergodic gas-like state, and leads to intermittent switching between states over long time. Overall, our work identifies nonreciprocal interactions as a key mechanism through which strongly-coupled finite systems transform interaction-mediated activity into dynamical nonequilibrium states.

cond-mat.soft↗

arXiv2026

DustNET: enabling machine learning and AI models of dusty plasmas

Dusty plasmas are ubiquitous throughout the universe, spanning laboratory and industrial plasmas, fusion devices, planetary environments, cometary comae, and interstellar media. Despite decades of research, many aspects of their behavior remain poorly understood within a unified framework. While numerous theoretical and numerical models describe specific phenomena, such as dust charging, transport, waves, and self-organization, fully predictive models across the wide range of spatial and temporal scales in both laboratory and natural systems remain elusive. Conventional plasma descriptions rely on coupled differential equations for particle densities, momenta, and energies, but their solutions are often limited by computational cost, numerical uncertainties, and incomplete knowledge of boundary conditions and transport processes. Recent advances in machine learning (ML), particularly deep neural networks, offer new opportunities to complement traditional physics-based modeling. Here we review ML and artificial intelligence (AI) approaches, termed bottom-up data-driven methods, for dusty plasma research. Central to this effort is Dust Neural nEtworks Technology (DustNET), a community-driven dataset initiative inspired by ImageNet, integrating experimental, simulation, and synthetic data to enable predictive modeling, uncertainty quantification, and multi-scale analysis. DustNET-trained models may also be deployed in real-time experimental settings under edge computing constraints. Combined with emerging multi-modal AI foundation models and autonomous agents, this framework provides a pathway toward a unified, physics-informed understanding of dusty plasmas across laboratory, industrial, space, and astrophysical environments.

physics.plasm-ph↗

arXiv2026

STL-to-Stokeslet Computation of Mobility Tensors and Sedimentation Dynamics for Shaped Particles

Sedimentation is extremely common in nature, occurring throughout the atmosphere and oceans, and in every laboratory centrifuge. The shape and mass distribution of a particle uniquely determines its motion at low Reynolds number, and complex dynamics can emerge from even simple particle shapes. The dynamics are governed by the particle's hydrodynamic mobility tensor, which dictates the translational and rotational velocities given the forces and torques. However, to date the inference of the mobility tensor from the object shape has been cumbersome and tricky. Starting with an input file representing an object for a 3D printer, such as an STL file, here we present an efficient numerical framework to compute the mobility tensor by discretizing the particle surface into distributed point drag forces called stokeslets. We validate our results against analytical solutions of simple geometries and recent experimental measurements. With our calculated mobility tensors in hand, using standard transformation laws, we demonstrate the dramatic effect of shifting the center of mass from a center of symmetry: all initial orientations evolve into one, two, or three particular final motions dictated by the object. By providing a user-friendly and efficient framework to compute the mobility tensor and resulting particle dynamics, this work offers a broadly applicable tool for the soft-matter, fluid-mechanics, and biophysics communities, and facilitates the design of steerable particles under diverse external forces, with relevance to colloidal transport, biological locomotion, diffusion, and self-assembly.

cond-mat.soft↗

arXiv2025

A microstructural rheological model for transient creep in polycrystalline ice

The slow creep of glacial ice plays a key role in sea-level rise, yet its transient deformation remains poorly understood. Glen's flow law, where strain rate is simply a function of stress, cannot predict the time-dependent creep behavior observed in experiments. Here we present a physics-based rheological model that captures all three regimes of transient creep in polycrystalline ice. The key components of the model are a series of Kelvin-Voigt mechanical elements that produce a power-law (Andrade) creep, and a single viscous element with microstructure and stress dependence that represents reorientation in the polycrystalline grains. The interplay between these components produces a minimum in the strain rate at approximately 1% strain, which is a universal but unexplained feature reported in experiments. Due to its transient nature, the model exhibits fractional power-law exponents in the stress dependence of the strain rate minimum, which has been conventionally interpreted as independent physical processes. Taken together, we provide a compact, mechanistic framework for transient ice rheology that generalizes to other polycrystalline materials and can be integrated into constitutive laws for ice-sheet models.

physics.geo-ph↗

arXiv2025

The electrostatic charge on exuded liquid drops

Fluid triboelectrification, also known as flow electrification, remains an under-explored yet ubiquitous phenomenon with potential applications from material science to planetary evolution. Building upon previous efforts to position water within the triboelectric series, we investigate the charge on individual, millimetric water drops falling through air. Our experiments measured the charge and mass of each drop using a Faraday cup mounted on a mass balance, and connected to an electrometer. For pure water in a glass syringe with a grounded metal tip, we find the charge per drop ($Δq/Δm$) was approximately -5 pC/g to -1 pC/g. This was independent of the release height of the drop, tip diameter and length, tip cleaning preparation, and whether the experiment was shielded with a Faraday cage. Biasing the tip to different voltages allowed for linear control of the drop charge, and the results were consistent with known electrochemical effects, namely the Volta potential expected between most metals and bulk water ($\approx$ -0.5 V). Introducing insulating plastic materials into the experiment (from the syringe body or tip) imparted large amounts of charge on the drops with systematic charge evolution. Together these results show that the flow electrification of water is more complex than previously reported, and is driven by material-dependent electrostatic processes.

cond-mat.soft↗

arXiv2025

Physics-tailored machine learning reveals unexpected physics in dusty plasmas

Dusty plasma is a mixture of ions, electrons, and macroscopic charged particles that is commonly found in space and planetary environments. The particles interact through Coulomb forces mediated by the surrounding plasma, and as a result, the effective forces between particles can be non-conservative and non-reciprocal. Machine learning (ML) models are a promising route to learn these complex forces, yet their structure should match the underlying physical constraints to provide useful insight. Here we demonstrate and experimentally validate an ML approach that incorporates physical intuition to infer force laws in a laboratory dusty plasma. Trained on 3D particle trajectories, the model accounts for inherent symmetries, non-identical particles, and learns the effective non-reciprocal forces between particles with exquisite accuracy (R^2>0.99). We validate the model by inferring particle masses in two independent yet consistent ways. The model's accuracy enables precise measurements of particle charge and screening length, discovering large deviations from common theoretical assumptions. Our ability to identify new physics from experimental data demonstrates how ML-powered approaches can guide new routes of scientific discovery in many-body systems. Furthermore, we anticipate our ML approach to be a starting point for inferring laws from dynamics in a wide range of many-body systems, from colloids to living organisms.

physics.plasm-ph↗

arXiv2024

Hyperelastic swelling of stiff hydrogels

Hydrogels are biphasic, swollen polymer networks where elastic deformation is coupled to nanoscale fluid flow. As a consequence, hydrogels can withstand large strains and exhibit nonlinear, hyperelastic properties. For low-modulus hydrogel and semiflexible biopolymer networks, previous studies have shown that these materials universally contract when sheared on timescales much longer than the poroelastic relaxation timescale. Using rheological and tribological measurements, we find that stiff polyacrylamide and polyacrylic acid hydrogels, with moduli of order ~10-100 kPa, exclusively swell (dilate) when sheared. The poroelastic relaxation process was examined using strain-controlled compression, indicating a volumetric diffusion constant of order 10^-9 m^2/s. Upon shearing, we observed an increase in normal stress that varied quadratically with shear strain, and persisted for hours. Moreover, we show that this dilatant behavior manifests as swelling during tribological sliding, imbibing the hydrogel with fluid. We suggest that this inherent, hyperelastic dilatancy is an important feature in all stiff hydrogels, and may explain rehydration and mechanical rejuvenation in biological tissues such as cartilage.

cond-mat.soft↗

arXiv2024

Coalescing Clusters Unveil New Regimes of Frictional Fluid Mechanics

Droplet coalescence is essential in a host of biological and industrial processes, involving complex systems as diverse as cellular aggregates, colloidal suspensions, and polymeric liquids. Classical solutions for the time evolution of coalescing clusters are typically based on tractable limiting physics, such as analytical solutions to the Stokes equation. By combining computational and theoretical analyses, we show that there is an unexplored family of coalescence processes: those governed by highly dissipative coupling to the environment. This leads to new scaling laws characterizing droplet coalescence, as well as new time-invariant parameterizations of the shape evolution of the coalescing system. We demonstrate these effects via particle-based simulations and both continuum and boundary-integral solutions to hydrodynamic equations, which we then understand in the context of a generalized Navier-Stokes-like equation. Our theoretical description of highly frictional coalescence mathematically maps onto Darcy flow in the presence of surface tension effects, opening up exciting avenues of research in applying well-studied fluid dynamical techniques to a broad range of novel systems.

cond-mat.soft↗

arXiv2023

Soft matter physics of the ground beneath our feet

Inspired by presentations by the authors during a workshop organized at the Princeton Center for Theoretical Science (PCTS) in January 2022, we present a perspective on some of the outstanding questions related to the "physics of the ground beneath our feet." These identified challenges are intrinsically shared with the field of Soft Matter but also have unique aspects when the natural environment is studied.

cond-mat.soft↗

arXiv2023

3D tracking of particles in a dusty plasma by laser sheet tomography

The collective behavior of levitated particles in a weakly-ionized plasma (dusty plasma) has raised significant scientific interest. This is due to the complex array of forces acting on the particles, and their potential to act as in-situ diagnostics of the plasma environment. Ideally, the three-dimensional (3D) motion of many particles should be tracked for long periods of time. Typically, stereoscopic imaging using multiple cameras combined with particle image velocimetry (PIV) is used to obtain a velocity field of many particles, yet this method is limited by its sample volume and short time scales. Here we demonstrate a different, high-speed tomographic imaging method capable of tracking individual particles. We use a scanning laser sheet coupled to a single high-speed camera. We are able to identify and track tens of individual particles over centimeter length scales for several minutes, corresponding to more than 10,000 frames.

physics.plasm-ph↗

arXiv2023

Hydrodynamic Collapse of the Leidenfrost Vapor Layer

During the Leidenfrost effect, a stable vapor film can separate a hot solid from an evaporating liquid. Eventually, after formation and upon cooling, the vapor layer cannot be sustained and undergoes a violent collapse evidenced by explosive boiling. Computationally, modeling this instability involves an interplay between hydrodynamics, thermodynamics, rapid evaporation, and length-scales from $μ$m to cm. Selective assumptions, made to reduce computational costs, have limited most previous studies to steady-state investigations. Here, we combine two-phase laminar flow, heat transfer, and evaporation in a finite-element simulation to examine the failure of Leidenfrost vapor layers during cooling. During periods of quiescence, the geometry of the vapor layer agrees well with steady-state lubrication theory. In the simulations, we report the local temperature of the solid at failure, $T_-$, which provides a lower bound for recent experimental work using the same geometric and material conditions. Surprisingly, we find that inertial forces, which are typically ignored in theoretical treatments of the vapor layer, are responsible for initiating the instability leading to failure.

physics.flu-dyn↗

arXiv2023

Dynamics of Mass Polar Spheroids During Sedimentation

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 symmetry axis and non-uniform density, recent theoretical predictions suggest that prolate objects will repel and oblate ones will weakly attract. We tested these predictions using mass polar proalte spheroids, which are composed of 2 mm spheres glued together. We probed different aspect rations ($κ$) and center of mass offsets ($χ$) by combining spheres of different densities. Experiments were done in both quasi-two-dimensional (2D) and three=dimensional (3D) chambers. By optically tracking the motion of single particles, we found that the dynamics were well-described by a reduced mobility matrix model that could be solved analytically. Pairs of particles exhibited an effective repulsion, and their separation roughly scaled as ~$(κ-1)/χ^{0.39}$, i.e. particles that were more prolate or had smaller mass asymmetry had stronger repulsion effects. In 3D, particles with $χ>0$ were distributed more uniformly than $χ=0$ particles, and the degree of uniformity increased with $κ$, indicating that the effective 2-body repulsion manifests for a large number of particles.

cond-mat.soft↗

arXiv2022

Extracting Forces from Noisy Dynamics in Dusty Plasmas

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 mostly tested on simulated data with known parameters. Here we use supervised machine learning to extract the electrostatic, hydrodynamic, and stochastic forces acting on micron-sized charged particles levitated in an argon plasma. Trained on simulated particle trajectories using more than 100 dynamical and statistical features, the model predicts system parameters with 50\% better accuracy than conventional methods, and provides non-contact measurements of the particle charge and Debye length.

cond-mat.soft↗

arXiv2022

The lifetime of charged dust in the atmosphere

Windblown dust plays critical roles in numerous geophysical and biological systems, yet current models fail to explain the transport of coarse-mode particles (>5 $μ$m) to great distances from their sources. For particles larger than a few microns, electrostatic effects have been invoked to account for longer-than-predicted atmospheric residence times. Although much effort has focused on elucidating the charging processes, comparatively little effort has been expended understanding the stability of charge on particles once electrified. Overall, electrostatic-driven transport requires that charge remain present on particles for days to weeks. Here, we present a set of experiments designed to explore the longevity of electrostatic charge on levitated airborne particles after a single charging event. Using an acoustic levitator, we measured the charge on particles of different material compositions suspended in atmospheric conditions for long periods of time. In dry environments, the total charge on particles decayed in over 1 week. The decay timescale decreased to days in humid environments. These results were independent of particle material and charge polarity. However, exposure to UV radiation could both increase or decrease the decay time depending on polarity. Our work suggests that the rate of charge decay on airborne particles is solely determined by ion capture from the air. Furthermore, using a one-dimensional sedimentation model, we predict that atmospheric dust of order 10 $μ$m will experience the largest change in residence time due to electrostatic forces.

physics.geo-ph↗

arXiv2021

The Minimum Leidenfrost Temperature on Smooth Surfaces

During the Leidenfrost effect, a thin insulating vapor layer separates an evaporating liquid from a hot solid. Here we demonstrate that Leidenfrost vapor layers can be sustained at much lower temperatures than those required for formation. Using a high-speed electrical technique to measure the thickness of water vapor layers over smooth, metallic surfaces, we find that the explosive failure point is nearly independent of material and fluid properties, suggesting a purely hydrodynamic mechanism determines this threshold. For water vapor layers of several millimeters in size, the minimum temperature for stability is $\approx 140^{\circ}$C, corresponding to an average vapor layer thickness of 10-20$μ$m.

physics.flu-dyn↗

arXiv2021

Relaxation and Recovery in Hydrogel Friction on Smooth Surfaces

\textbf{Background} Hydrogels are crosslinked polymer networks that can absorb and retain a large fraction of liquid. Near a critical sliding velocity, hydrogels pressed against smooth surfaces exhibit time-dependent frictional behavior occurring over multiple timescales, yet the origin of these dynamics is unresolved. \textbf{Objective} Here, we characterize this time-dependent regime and show that it is consistent with two distinct molecular processes: sliding-induced relaxation and quiescent recovery. \textbf{Methods} Our experiments use a custom pin-on-disk tribometer to examine poly(acrylic acid) hydrogels on smooth poly(methyl methacrylate) surfaces over a variety of sliding conditions, from minutes to hours. \textbf{Results} We show that at a fixed sliding velocity, the friction coefficient decays exponentially and reaches a steady-state value. The time constant associated with this decay varies exponentially with the sliding velocity, and is sensitive to any precedent frictional shearing of the interface. This process is reversible; upon cessation of sliding, the friction coefficient recovers to its original state. We also show that the initial direction of shear can be imprinted as an observable "memory", and is visible after 24 hrs of repeated frictional shearing. \textbf{Conclusions} We attribute this behavior to nanoscale extension and relaxation dynamics of the near-surface polymer network, leading to a model of frictional relaxation and recovery with two parallel timescales.

cond-mat.soft↗

arXiv2020

The origin of large amplitude oscillations of dust particles in a plasma sheath

Micron-size charged particles can be easily levitated in low-density plasma environments. At low pressures, suspended particles have been observed to spontaneously oscillate around an equilibrium position. In systems of many particles, these oscillations can catalyze a variety of nonequilibrium, collective behaviors. Here, we report spontaneous oscillations of single particles that remain stable for minutes with striking regularity in amplitude and frequency. The oscillation amplitude can also exceed 1 cm, nearly an order of magnitude larger than previously observed. Using an integrated experimental and numerical approach, we show how the motion of an individual particle can be used to extract the electrostatic force and equilibrium charge variation in the plasma sheath. Additionally, using a delayed-charging model, we are able to accurately capture the nonlinear dynamics of the particle motion, and estimate the particle's equilibrium charging time in the plasma environment.

physics.plasm-ph↗