Jigging: A Review of Fundamentals and Future Directions

For centuries, jigging has been a workhorse of the mineral processing industry. Recently, it has also found its way into the recycling industry, and the increasing concerns related to water usage has led to a renewed interest in dry jigging. However, the current scenario of increasing ore complexity and the advent of smart sensor technologies, such as sensor-based sorting (SBS), has established increasingly challenging levels for traditional concentration methods, such as jigging. Against this background, the current review attempts to summarize and refresh the key aspects and concepts about jigging available in the literature. The configuration, operational features, applications, types, and theoretical models of jigging are comprehensively reviewed. Three promising paths for future research are presented: (1) using and adapting concepts from granular physics in fundamental studies about the stratification phenomena in jigs; (2) implementing advanced control functions by using machine vision and multivariate data analysis and; (3) further studies to unlock the potential of dry jigs. Pursuing these and other innovations are becoming increasingly essential to keep the role of jigging as a valuable tool in future industry.

Mechanical forces drive ordered patterning of hair cells in the mammalian inner ear

Periodic organization of cells is required for the function of many organs and tissues. The development of such periodic patterns is typically associated with mechanisms based on intercellular signaling such as lateral inhibition and Turing patterning. Here we show that the transition from disordered to ordered checkerboard-like pattern of hair cells and supporting cells in the mammalian hearing organ, the organ of Corti, is likely based on mechanical forces rather than signaling events. Using time-lapse imaging of mouse cochlear explants, we show that hair cells rearrange gradually into a checkerboard-like pattern through a tissue-wide shear motion that coordinates intercalation and delamination events. Using mechanical models of the tissue, we show that global shear and local repulsion forces on hair cells are sufficient to drive the transition from disordered to ordered cellular pattern. Our findings suggest that mechanical forces drive ordered hair cell patterning in a process strikingly analogous to the process of shear-induced crystallization in polymer and granular physics.

Scanning geophysical hazards

Granular physics, the study of how collections of macroscopic particles behave en masse, helps us to model geophysical hazards like snow avalanches and landslides. Before placing trust in any predictions, we need a complete picture of how opaque grains flow. X-ray technologies provide an unobtrusive means to see beyond the surface. Whereas classical tomography does not work for moving samples, new dynamic X-ray approaches can handle genuinely flowing regimes, offering fresh insight.

Mitigating memory effects during undulatory locomotion on hysteretic materials

Undulatory swimming in flowing media like water is well-studied, but little is known about loco-motion in environments that are permanently deformed by body–substrate interactions like snakes in sand, eels in mud, and nematode worms in rotting fruit. We study the desert-specialist snake Chion-actis occipitalis traversing granular matter and find body inertia is negligible despite rapid transit and speed dependent granular reaction forces. New surface resistive force theory (RFT) calculation reveals how this snakes wave shape minimizes memory effects and optimizes escape performance given physiological limitations (power). RFT explains the morphology and waveform dependent performance of a diversity of non-sand-specialist, but overpredicts the capability of snakes with high slip. Robophysical experiments recapitulate aspects of these failure-prone snakes and elucidate how reencountering previously remodeled material hinders performance. This study reveals how memory effects stymied the locomotion of a diversity of snakes in our previous studies [Marvi et al, Science, 2014] and suggests the existence of a predictive model for history-dependent granular physics.

Understanding earthquake from the granular physics point of view — Causes of earthquake, earthquake precursors and predictions

We treat the earth crust and mantle as large scale discrete matters based on the principles of granular physics and existing experimental observations. Main outcomes are: A granular model of the structure and movement of the earth crust and mantle is established. The formation mechanism of the tectonic forces, which causes the earthquake, and a model of propagation for precursory information are proposed. Properties of the seismic precursory information and its relevance with the earthquake occurrence are illustrated, and principle of ways to detect the effective seismic precursor is elaborated. The mechanism of deep-focus earthquake is also explained by the jamming–unjamming transition of the granular flow. Some earthquake phenomena which were previously difficult to understand are explained, and the predictability of the earthquake is discussed. Due to the discrete nature of the earth crust and mantle, the continuum theory no longer applies during the quasi-static seismological process. In this paper, based on the principles of granular physics, we study the causes of earthquakes, earthquake precursors and predictions, and a new understanding, different from the traditional seismological viewpoint, is obtained.