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1. Vibrational manipulation of dry granular materials in lab-on-a-chip devices

   https://doi.org/10.1039/D3LC00722G  

   Hui, Timothy C; Zhang, Xiaolin; Adiga, Dhruva; Miller, Gregory H; Ristenpart, William D (February 2024, Lab on a Chip)

   We present vibrational techniques to pump, mix, and separate dry granular materials using multifrequency vibrations applied to a solid substrate with a standard audio system. 

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2. A Comprehensive Survey on Rare Event Prediction

   https://doi.org/10.1145/3699955  

   Shyalika, Chathurangi; Wickramarachchi, Ruwan; Sheth, Amit P (March 2025, ACM Computing Surveys)

   Rare event prediction involves identifying and forecasting events with a low probability using machine learning (ML) and data analysis. Due to the imbalanced data distributions, where the frequency of common events vastly outweighs that of rare events, it requires using specialized methods within each step of the ML pipeline, that is, from data processing to algorithms to evaluation protocols. Predicting the occurrences of rare events is important for real-world applications, such as Industry 4.0, and is an active research area in statistical and ML. This article comprehensively reviews the current approaches for rare event prediction along four dimensions: rare event data, data processing, algorithmic approaches, and evaluation approaches. Specifically, we consider 73 datasets from different modalities (i.e., numerical, image, text, and audio), four major categories of data processing, five major algorithmic groupings, and two broader evaluation approaches. This article aims to identify gaps in the current literature and highlight the challenges of predicting rare events. It also suggests potential research directions, which can help guide practitioners and researchers. 

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3. Fully dense and cohesive FCC granular crystals

   https://doi.org/10.1016/j.eml.2024.102208  

   Karuriya, Ashta Navdeep; Simoes, Jeremy; Barthelat, Francois (September 2024, Extreme Mechanics Letters)

   Typical granular materials are far from optimal in terms of mechanical performance: Random packing leads to poor load transfer in the form of thin and dispersed force lines within the material, to inhomogeneous jamming, and to strain localization. In addition, localized contacts between individual grains result in low stiffness, strength and brittleness. Here we propose a granular material that simultaneously embodies three approaches to increase strength: geometrical design of individual grains, crystallization, and infiltration by an adhesive. Using mechanical vibrations, we assembled millimeter-scale 3D printed grains with rhombic dodecahedral shapes into fully dense FCC granular crystals. We then infiltrated the granular structure with a tacky, polyacrylic adhesive that is orders of magnitude weaker than the grains, but which provides sustained adhesion over large interfacial displacements. The resulting material is a fully dense, free-standing space filling granular crystal. Compressive tests show that these granular crystals are up to 60 times stronger than randomly packed cohesive spheres and they display a rich set of mechanisms: Nonlinear deformations, crystal plasticity reminiscent of atomistic mechanisms, cross-slip, shear-induced dilatancy, micro-buckling, and tensile strength. To capture some of these mechanisms we developed a multiscale model that incorporates local cohesion between grains, resolved shear and normal stresses on available slip planes, and prediction of compressive strength as function of loading orientation. The predicted strength is highly anisotropic and agrees well with the compression experiments. Once fully understood and harnessed, we envision that these mechanisms will lead to granular engineering materials with unusual combinations of mechanical performances attractive for many applications. 

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4. Elastic bit and Berry phase: Investigating topological phenomena in a classical granular network

   https://doi.org/10.1121/10.0027701  

   Hasan, M Arif; Mahmood, Kazi Tahsin (March 2024, The Journal of the Acoustical Society of America)

   This study investigates the Berry phase, a key concept in classical and quantum physics, and its manifestation in a classical system. We achieve controlled accumulation of the Berry phase by manipulating the elastic bit (a classical analogue to a quantum bit) in an externally driven, homogeneous, spherical, nonlinear granular network. This is achieved through the classical counterpart of quantum coherent superposition of states. The elastic bit's state vectors are navigated on the Bloch sphere using external drivers' amplitude, phase, and frequency, yielding specific Berry phases. These phases distinguish between trivial and nontrivial topologies of the elastic bit, with the zero Berry phase indicating pure states of the linearized granular system and the nontrivial π phase representing equal superposed states. Other superposed states acquire different Berry phases. Crucially, these phases correlate with the structure's eigenmode vibrations: trivial phases align with distinct, in-phase, or out-of-phase eigenmodes, while nontrivial phases correspond to coupled vibrations where energy is shared among granules, alternating between oscillation and rest. Additionally, we explore Berry's phase generalizations for non-cyclic evolutions. This research paves the way for advanced quantum-inspired sensing and computation applications by utilizing and controlling the Berry phase. 

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5. Size-dependence of the flow threshold in dense granular materials

   https://doi.org/10.1039/c8sm00843d  

   Liu, Daren; Henann, David L. (January 2018, Soft Matter)

   The flow threshold in dense granular materials is typically modeled by local, stress-based criteria. However, grain-scale cooperativity leads to size effects that cannot be captured with local conditions. In a widely studied example, flows of thin layers of grains down an inclined surface exhibit a size effect whereby thinner layers require more tilt to flow. In this paper, we consider the question of whether the size-dependence of the flow threshold observed in inclined plane flow is configurationally general. Specifically, we consider three different examples of inhomogeneous flow – planar shear flow with gravity, annular shear flow, and vertical chute flow – using two-dimensional discrete-element method calculations and show that the flow threshold is indeed size-dependent in these flow configurations, displaying additional strengthening as the system size is reduced. We then show that the nonlocal granular fluidity model – a nonlocal continuum model for dense granular flow – is capable of quantitatively capturing the observed size-dependent strengthening in all three flow configurations. 

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