Siamak Seyfi

Block arrays are widely used as laboratory roughness elements, yet how block size and arrangement affect downstream turbulence and momentum transport remains poorly understood. Particle image velocimetry measurements downstream of five beds in subcritical open-channel flow (Re=3.51×104 and Fr=0.08) compare a no-block (NB) reference with four near-cubic block arrays: small aligned (SA), small staggered (SS), large aligned (LA), and large staggered (LS). The approach flow is conditioned by a square-bar turbulence-generating grid, so the study characterizes how a finite block array reorganizes controlled, grid-generated turbulence. All arrays redistribute momentum vertically, producing near-block velocity deficits with faster flow above; block size dominates, larger blocks generating deeper, more intense deficits. Unlike NB, all rough cases sustain predominantly positive Reynolds shear stress across the domain, indicating continuous downward momentum transport attributed to block-generated shear layers and wakes. Turbulent kinetic energy production increases with block size, by 9.0% (SA), 28.6% (SS), 322.2% (LS), and 375.9% (LA) relative to NB. Integral length scales decrease in all rough cases, and quadrant decomposition confirms a shift toward ejection-sweep (Q2–Q4) dominance with geometry-dependent vertical penetration. Sweeps are more frequent near the blocks, but ejections are more energetic and dominate the Reynolds shear stress, even under hole filtering. Within the present conditioned-inflow configuration, block size governs the magnitude and vertical extent of roughness-induced turbulence, while arrangement modulates its spatial organization, guiding block-array design in laboratory open-channel flows.

The hydrodynamic settling of microplastic particles is yet to be explored in-depth, particularly in multiple-particle systems where wake interactions may affect settling dynamics. Here, we present an experimental study investigating the settling of spherical particles (∼1.92 mm, 1300 kg m−3) across a transitional particle Reynolds number range (Rep=6–185), relevant to microplastic transport. Using a dual-camera back-lit shadow-imaging system and Particle Tracking Velocimetry, we quantify single-particle terminal velocities and analyze pairwise interactions. Our findings reveal that trailing particles accelerate when entering the wake of a leading particle, with peak enhancements up to 68% above the single-particle terminal velocity. Interaction cases are characterized based on vertical and lateral spacing. In the key observed interaction pattern, drafting begins at a vertical distance of approximately two particle diameters and is accompanied by coupled lateral motion, indicating two-way interaction. These results demonstrate that particle–particle coupling during the drafting stage is stronger and more complex than predicted by classical models, and suggest that even weakly inertial microplastics can experience enhanced settling due to interparticle proximity. This work provides a foundation for improved predictive modeling of microplastic transport in aquatic environments.

Microrobots have attracted the attention of scientists owing to their unique features to accomplish tasks in hard-to-reach sites in the human body. Microrobots can be precisely actuated and maneuvered individually or in a swarm for cargo delivery, sampling, surgery, and imaging applications. In addition, microrobots have found applications in the environmental sector (e.g., water treatment). Besides, recent advancements of three-dimensional (3D) printers have enabled the high-resolution fabrication of microrobots with a faster design-production turnaround time for users with limited micromanufacturing skills. Here, the latest end applications of 3D printed microrobots are reviewed (ranging from environmental to biomedical applications) along with a brief discussion over the feasible actuation methods (e.g., on- and off-board), and practical 3D printing technologies for microrobot fabrication. In addition, as a future perspective, we discussed the potential advantages of integration of microrobots with smart materials, and conceivable benefits of implementation of artificial intelligence (AI), as well as physical intelligence (PI). Moreover, in order to facilitate bench-to-bedside translation of microrobots, current challenges impeding clinical translation of microrobots are elaborated, including entry obstacles (e.g., immune system attacks) and cumbersome standard test procedures to ensure biocompatibility.