When a highly elastic drop of a polymer solution hits a superhydrophobic surface at a high speed, a growing tail-like filament emerges vertically from the impact spot as the contact line recedes. Notably, the ligament transitions into a balloon-like shape before detaching completely from the surface (Balloon regime). The ligament formation is attributed to liquid impalement upon impact into the surface protrusion spacing, and elastic forces due to polymers prevent ligament breakup. The detachment of the ligament happens when polymeric stresses balance or overcome the adhesion at the surface. This study shows that tuning droplet rheology and surface roughess enables droplets to rebound completely and without splashing at high impact speeds. 

When a highly elastic drop of a polymer solution hits a superhydrophobic surface at a high speed, a growing tail-like filament emerges vertically from the impact spot as the contact line recedes. Notably, the ligament transitions into a balloonlike shape before detaching completely from the surface (balloon regime). The ligament formation is attributed to liquid impalement upon impact into the surface protrusion spacing, and elastic forces due to polymers prevent ligament breakup. The detachment of the ligament happens when polymeric stresses balance or overcome the adhesion at the surface. This study shows that tuning droplet rheology and surface roughness enables droplets to rebound completely and without splashing at high impact speeds.

Three-dimensional (3D) microfabrication/nanofabrication technologies have revolutionized various fields by enabling the precise construction of complex microstructures/nanostructures1, 2, 3, 4, 5–6. However, existing methods face challenges in fabricating intricate 3D architectures from a diverse range of materials beyond conventional polymers. Here we introduce a universal 3D microfabrication/nanofabrication strategy compatible with a broad range of materials by precisely manipulating optofluidic interactions within a confined 3D space, enabling the creation of volumetric, free-form 3D microstructures/nanostructures. A femtosecond-laser-induced heating spot generates a localized thermal gradient, providing precise spatiotemporal control over optofluidic interactions of the nanoparticle-laden dispersions. This enables the rapid and highly localized assembly of nanoparticles with diverse shapes and compositions—including metals, metal oxides, carbon nanomaterials and quantum dots—into complex 3D microstructures. To demonstrate its versatility, we fabricate multifunctional microdevices, such as 3D microfluidic valves with size-selective sieving functionality, achieving fast separation of microparticles/nanoparticles with distinct dimensions, as well as microrobots integrated with four distinct functional materials, achieving multimodal locomotion powered by different external stimuli. This optofluidic 3D microfabrication/nanofabrication method unlocks new opportunities for advanced material innovation and miniaturized device development, paving the way for broad applications in colloidal robotics7, microphotonics/nanophotonics, catalysis and microfluidics.

Results of direct numerical simulations (DNS) of open channel incompressible turbulent flows over porous lattices are reported in this work. Heat transfer is included as a passive temperature scalar field which is advected by the velocity field but does not affect it. The evolution of the temperature field in both fluid and solid phases is considered, thus solving for the conjugate problem of heat transfer. The fluid-solid combination considered here is that of air and aluminum, which is common to practical cooling applications. Similar to Rouhi et al. (2022), the thermal performance is assessed in terms of the Reynolds analogy breakdown, which is the disparity between the fractional increases in the Stanton number, St, and the fractional increases in the skin-friction coefficient, C_f, relative to a baseline smooth-wall case. The breakdown is in general unfavorable over the porous substrates, similar to rough walls. Unlike rough walls however, a limit in heat transfer is not reached over the porous substrates for a similar amount of fractional increase in C_f. How much of a maximum gain in heat transfer can be achieved however remains to be determined. The unfavorable breakdown in Reynolds analogy is attributable to growing dissimilarities between momentum and heat transport in the vicinity of a substrate's surface as it becomes more permeable. Turbulent sweep and ejection type events contribute much more to the momentum transport across the permeable surface than they do to heat. Additionally, the change in heat transfer over the porous substrates is not monotonic. The total heat flux initially decreases when going from a conductive smooth wall to slightly porous walls. In this initial porous-wall regime, the near-wall flow is canonical in structure and the heat transfer is dominated by molecular diffusion. As such, a reduction of the more favorably conducting solid material diminishes the overall heat transfer performance. Beyond a certain level of permeability however, where the near-wall flow transitions to the K-H-like regime marked by the presence of cross-stream rollers, the heat flux undergoes an increasing trend and eventually surpasses that of the smooth-wall case. Therefore, depending upon the cooling configuration being considered, an assessment must first be made as to whether or not the heat transfer influence of the solid phase can be neglected. Otherwise, failing to take into account the thermal behavior of the solid material can result in overestimations of any gains in heat transfer.

 When water droplets impact solid surfaces at high velocity, they often develop radial protrusions—known as fingering instabilities—that subsequently break up during spreading and retraction, a process termed splashing. Here, we investigate the fingering dynamics of shear‑thinning viscoelastic droplets impacting superhydrophobic surfaces. At low polymer concentrations, liquid elasticity sustains the emergence of elongated fingers, while simultaneously stabilizing them against breakup, thereby suppressing splashing. In contrast, increasing polymer concentration enhances viscous damping, reducing the number of fingers and ultimately suppressing the fingering instability. Our results indicate that the onset of fingering is governed by the interplay of inertia, surface tension, and viscous stresses, while the number of fingers scales robustly with the Weber number. This highlights the dominance of inertia-capillary dynamics in our range of Weber number once the instability is triggered. Remarkably, all impact outcomes resulted in complete rebound, in contrast to previous observation for viscoelastic droplets. Finally, we employ a theoretical framework to predict the temporal evolution of the mean ligament length across polymer concentrations, providing quantitative insight into how elasticity modifies drop retraction dynamics.

When water droplets impact solid surfaces at high velocity, they often develop radial protrusions, known as fingering instabilities, that subsequently break up during spreading and retraction, a process termed splashing. Here, we investigate the fingering dynamics of shear-thinning viscoelastic droplets impacting superhydrophobic surfaces. At low polymer concentrations, liquid elasticity promotes the emergence of elongated fingers while simultaneously stabilizing them against breakup, thereby suppressing splashing. In contrast, an increasing polymer concentration enhances viscous damping, reducing the number of fingers and ultimately suppressing the fingering instability. Our results indicate that the onset of fingering is governed by the interplay of inertia, surface tension, and viscous stresses, while the number of fingers scales robustly with the Weber number. This highlights the dominance of inertia–capillary dynamics in our range of Weber numbers once the instability is triggered. Remarkably, all impact outcomes resulted in complete rebound, in contrast to a previous observation for viscoelastic droplets. Finally, we employ a theoretical framework to predict the temporal evolution of the mean ligament length across polymer concentrations, providing quantitative insight into how elasticity modifies drop retraction dynamics.

The transition to a sustainable, low-carbon energy future requires transformative advancements in energy and environmental technologies. Carbon capture and sequestration, underground hydrogen storage, and nuclear waste geological disposal will be central aspects of a sustainable energy future, which hinge on a hidden world: reactive multiphase flows in opaque, heterogeneous porous media. Despite their foundational importance, the pore-scale dynamics that govern these technologies remain elusive. Here, we argue that microfluidic porous media are emerging as transformative platforms for the direct visualization of multiphase reactive flow in porous media and eventually optimizing these multiple physicochemical and biological processes. This review highlights critical scientific challenges associated with these sustainable energy solutions and summarizes the state-of-the-art microfluidic techniques for studying the interplay between multiphase flow, reactive transport, and biological effects in porous media. We also propose promising microfluidic technologies to support sustainable energy applications further. By offering a comprehensive overview of how microfluidic approaches deepen our understanding of fundamental pore-scale dynamics and connect them to large-scale behavior, this review is expected to promote both experimental and theoretical understanding of multiphase reactive flow in porous media, thereby informing material design, process optimization, and predictive modeling for scalable implementation. By fostering interdisciplinary collaboration across microfluidics, fluid mechanics, geophysics, materials science, and subsurface engineering, we hope to accelerate innovation and advance sustainable energy solutions.

Culturing living cells in three-dimensional environments increases the biological relevance of laboratory experiments, but requires solutes to overcome a diffusion barrier to reach the centre of cellular constructs. We present a theoretical and numerical investigation that brings a mechanistic understanding of how microfluidic culture conditions, including chamber size, inlet fluid velocity and spatial confinement, affect solute distribution within three-dimensional cellular constructs. Contact with the chamber substrate reduces the maximally achievable construct radius by 15%. In practice, finite diffusion and convection kinetics in the microfluidic chamber further lower that limit. The benefits of external convection are greater if transport rates across diffusion-dominated areas are high. Those are omnipresent and include the diffusive boundary layer growing from the fluid-construct interface and regions near corners where fluid is recirculating. Such regions multiply the required convection to achieve a given solute penetration by up to 100, so chip designs ought to minimize them. Our results define conditions where complete solute transport into an avascular three-dimensional cell construct is achievable and applies to real chambers without needing to simulate their exact geometries.

Rough surfaces are prevalent in flow-related applications due to surface degradation. The roughness topography can alter the surface skin friction and heat transfer in turbulent flows. Depending on the different mechanism of the roughness formation process, the roughness topography may exhibit anisotropic properties. The present work aims to shed light on the effect of roughness an isotropy on skin friction and heat transfer by systematically varying roughness properties in different directions and across various scales. To this end, irregular anisotropic rough surfaces are generated based on 2-D power spectrum (PS). The surfaces are generated with Gaussian height probability density functions (PDF) and with either matched surface anisotropy ratios (SAR=𝐿𝐶𝑜𝑟𝑟𝑥 ∕𝐿𝐶𝑜𝑟𝑟𝑧 ) or effective slope ratios (ESR=𝐸𝑆𝑥∕𝐸𝑆𝑧). By adjusting the 2-D PS, the degree of anisotropy is varied at different wavenumbers, some surfaces are more anisotropic at large scales and some at small scales. Direct numerical simulations are performed to study turbulent flow over these anisotropic rough surfaces at Re𝜏 = 500, 𝑃 𝑟 = 0.71. The results demonstrate that the roughness an isotropy play a pivotal role in influencing both skin friction and heat transfer of the rough surface, leading to alterations of up to more than 50% in the roughness function 𝛥𝑈 +and the temperature roughness function 𝛥𝛩+. Detailed analysis indicates that commonly used parameters, SARor ESR alone, may not be the most appropriate predictive quantities to characterize the effects of an isotropicirregular roughness. In light of this, we introduce a new roughness topographical parameter 𝜂SA= ESR/SAR that successfully correlates with the observed anisotropic effect. The suitability of this new parameter is assessed through comprehensive analysis of both the current dataset and the an isotropic roughness from literature.

Efficient tools for predicting the drag of rough walls in turbulent flows would have a tremendous impact. However, accurate methods for drag prediction rely on experiments or numerical simulations which are costly and time consuming. Data-driven regression methods have the potential to provide a prediction that is accurate and fast. We assess the performance and limitations of linear regression, kernel methods and neural networks for drag prediction using a database of 1000 homogeneous rough surfaces. Model performance is evaluated using the roughness function obtained at a friction Reynolds number $Re_\tau$ of 500. With two trainable parameters, the kernel method can fully account for nonlinear relations between the roughness function $\Delta U<^>+$ and surface statistics (roughness height, effective slope, skewness, etc.). In contrast, linear regression cannot account for nonlinear correlations and displays large errors and high uncertainty. Multilayer perceptron and convolutional neural networks demonstrate performance on par with the kernel method but have orders of magnitude more trainable parameters. For the current database size, the networks' capacity cannot be fully exploited, resulting in reduced generalizability and reliability. Our study provides insight into the appropriateness of different regression models for drag prediction. We also discuss the remaining steps before data-driven methods emerge as useful tools in applications.