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1. The structural, vibrational, and mechanical properties of jammed packings of deformable particles in three dimensions

   https://doi.org/10.1039/d1sm01228b  

   Wang, Dong; Treado, John D.; Boromand, Arman; Norwick, Blake; Murrell, Michael P.; Shattuck, Mark D.; O'Hern, Corey S. (November 2021, Soft Matter)

   We investigate the structural, vibrational, and mechanical properties of jammed packings of deformable particles with shape degrees of freedom in three dimensions (3D). Each 3D deformable particle is modeled as a surface-triangulated polyhedron, with spherical vertices whose positions are determined by a shape-energy function with terms that constrain the particle surface area, volume, and curvature, and prevent interparticle overlap. We show that jammed packings of deformable particles without bending energy possess low-frequency, quartic vibrational modes, whose number decreases with increasing asphericity and matches the number of missing contacts relative to the isostatic value. In contrast, jammed packings of deformable particles with non-zero bending energy are isostatic in 3D, with no quartic modes. We find that the contributions to the eigenmodes of the dynamical matrix from the shape degrees of freedom are significant over the full range of frequency and shape parameters for particles with zero bending energy. We further show that the ensemble-averaged shear modulus 〈 G 〉 scales with pressure P as 〈 G 〉 ∼ P β , with β ≈ 0.75 for jammed packings of deformable particles with zero bending energy. In contrast, β ≈ 0.5 for packings of deformable particles with non-zero bending energy, which matches the value for jammed packings of soft, spherical particles with fixed shape. These studies underscore the importance of incorporating particle deformability and shape change when modeling the properties of jammed soft materials. 

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2. Analyzing collective motion with machine learning and topology

   https://doi.org/10.1063/1.5125493  

   Bhaskar, Dhananjay; Manhart, Angelika; Milzman, Jesse; Nardini, John_T; Storey, Kathleen_M; Topaz, Chad_M; Ziegelmeier, Lori (December 2019, Chaos: An Interdisciplinary Journal of Nonlinear Science)

   We use topological data analysis and machine learning to study a seminal model of collective motion in biology [M. R. D’Orsogna et al., Phys. Rev. Lett. 96, 104302 (2006)]. This model describes agents interacting nonlinearly via attractive-repulsive social forces and gives rise to collective behaviors such as flocking and milling. To classify the emergent collective motion in a large library of numerical simulations and to recover model parameters from the simulation data, we apply machine learning techniques to two different types of input. First, we input time series of order parameters traditionally used in studies of collective motion. Second, we input measures based on topology that summarize the time-varying persistent homology of simulation data over multiple scales. This topological approach does not require prior knowledge of the expected patterns. For both unsupervised and supervised machine learning methods, the topological approach outperforms the one that is based on traditional order parameters. 

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3. Short Time Large Deviations of the KPZ Equation

   https://doi.org/doi.org/10.1007/s00220-021-04050-w  

   Lin, Yier; Tsai, Li-Cheng (August 2021, Communications in mathematical physics)

   We establish the Freidlin–Wentzell Large Deviation Principle (LDP) for the Stochastic Heat Equation with multiplicative noise in one spatial dimension. That is, we introduce a small parameter ε√ to the noise, and establish an LDP for the trajectory of the solution. Such a Freidlin–Wentzell LDP gives the short-time, one-point LDP for the KPZ equation in terms of a variational problem. Analyzing this variational problem under the narrow wedge initial data, we prove a quadratic law for the near-center tail and a 52 law for the deep lower tail. These power laws confirm existing physics predictions (Kolokolov and Korshunov in Phys Rev B 75(14):140201, 2007, Phys Rev E 80(3):031107, 2009; Meerson et al. in Phys Rev Lett 116(7):070601, 2016; Le Doussal et al. in Phys Rev Lett 117(7):070403, 2016; Kamenev et al. in Phys Rev E 94(3):032108, 2016). 

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4. Wavelength scaling of electron collision times in filament-produced plasma in solids

   Nagar, G C; Dempsey, D; Shim, B (June 2021, Bulletin of the American Physical Society)

   null (Ed.)

   We report an anomalous regime of laser-matter interactions, which is created by the wavelength dependence of electron collision time during filamentation in solids. Experiments are performed using femtosecond-time-resolved interferometry by varying the filament driver wavelength from 1.2 to 2.3 μm and using a 0.8-μm probe. Information on the phase and absorption via interferometry enables simultaneous measurements of plasma densities and electron collision times during filamentation. Although it is expected that the plasma density decreases with increasing wavelength due to larger plasma-defocusing at longer wavelengths [1-4], our measured plasma densities are nearly constant for all the pump wavelengths. This observation is successfully explained by the measured wavelength-dependence of electron collision time: electron collision times in filament-produced plasma decrease with increasing wavelength, which creates an anomalous regime of plasma-defocusing where longer wavelengths experience smaller plasma defocusing. In addition, simulations with the measured electron collision times successfully reproduce the observed plasma density scaling with wavelength [5]. [1] L. Bergé et al., Phys. Rev. A 88, 023816 (2013). [2] Y. E. Geints et al., Appl. Opt. 56, 1397 (2017). [3] S. Tochitsky et al., Nat. Photonics 13, 41 (2019). [4] R. I. Grynko et al., Phys. Rev. A 98, 023844 (2018). [5] Nagar et al., submitted. 

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5. Modeling nanoparticle charge distribution in the afterglow of non-thermal plasmas and comparison with measurements

   https://doi.org/https://doi.org/10.1088/1361-6463/abf70c  

   Suresh, Vikram; Li, Li; Redmond Go Felipe, Joshua; Gopalakrishnan, Ranganathan (June 2021, Journal of physics)

   null (Ed.)

   Particle charging in the afterglows of non-thermal plasmas typically take place in a non-neutral space charge environment. We model the same by incorporating particle-ion collision rate constant models, developed in prior work by analyzing particle-ion trajectories calculated using Langevin Dynamics simulations, into species transport equations for ions, electrons and charged particles in the afterglow. A scaling analysis of particle charging and additional Langevin Dynamics calculations of the particle-ion collision rate constant are presented to extend the range of applicability to ion electrostatic to thermal energy ratios of 300 and diffusive Knudsen number (that scales inversely with gas pressure) up to 2000. The developed collision rate constant models are first validated by comparing predictions of particle charge against measured values in a stationary, non-thermal DC plasma from past PK-4 campaigns published in Phys. Rev. Lett. 93(8): 085001 and Phys. Rev. E 72(1): 016406). The comparisons reveal excellent agreement within ±35% for particles of radius 0.6,1.0,1.3 μm in the gas pressure range of ~20-150 Pa. The experiments to probe particle charge distributions by Sharma et al. (J. Physics D: Appl. Phys. 53(24): 245204) are modeled using the validated particle-ion collision rate constant models and the calculated charge fractions are compared with measurements. The comparisons reveal that the ion/electron concentration and gas temperature in the afterglow critically influence the particle charge and the predictions are generally in qualitative agreement with the measurements. Along with critical assessment of the modeling assumptions, several recommendations are presented for future experimental design to probe charging in afterglows. 

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