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Computational fluid dynamics models often employ the free shear boundary condition at free surfaces, a result from the continuity of the stress and the large viscosity contrast at liquid–gas interfaces. This study leverages nonequilibrium molecular dynamics simulations to investigate the validity of the free shear boundary condition on the exposed surface of a liquid meniscus at the nanoscale. The primary objective is elucidating the fundamental mechanisms and behavior of fluid interactions within a capillary meniscus formed between two carbon nanotubes (CNTs) in shear-driven flow. Shear-driven flow simulations were conducted by varying the velocity of a solid slab to induce different shear rates in the adjacent water molecules. The results demonstrate, for the first time, negligible shear at the free surface, supporting the free shear assumption from the nanoscale point of view. A force balance analysis reveals that capillary and surface tension forces dominate within the meniscus, dictating its shape and stability. Meniscus deformation was observed and primarily attributed to interatomic interactions between water molecules and CNTs, driven by a combination of short-range repulsive forces and van der Waals attractions. The minimal contribution from shear forces suggests that interatomic forces, rather than applied shear stress, are the primary drivers of the meniscus deformation. These findings offer valuable insights into fluid behavior and a sound fundamental analysis of the free shear boundary condition at the nanoscale. 

This paper reports on the effects of shear rate and interface modeling parameters on the hydrodynamic slip length (LS) for water–graphite interfaces calculated using non-equilibrium molecular dynamics. Five distinct non-bonded solid–liquid interaction parameters were considered to assess their impact on LS. The interfacial force field derivations included sophisticated electronic structure calculation-informed and empirically determined parameters. All interface models exhibited a similar and bimodal LS response when varying the applied shear rate. LS in the low shear rate regime (LSR) is in good agreement with previous calculations obtained through equilibrium molecular dynamics. As the shear rate increases, LS sharply increases and asymptotes to a constant value in the high shear regime (HSR). It is noteworthy that LS in both the LSR and HSR can be characterized by the density depletion length, whereas solid–liquid adhesion metrics failed to do so. For all interface models, LHSR calculations were, on average, ∼28% greater than LLSR, and this slip jump was confirmed using the SPC/E and TIP4P/2005 water models. To address the LS transition from the LSR to the HSR, the viscosity of water and the interfacial friction coefficient were investigated. It was observed that in the LSR, the viscosity and friction coefficient decreased at a similar rate, while in the LSR-to-HSR transition, the friction coefficient decreased at a faster rate than the shear viscosity until they reached a new equilibrium, hence explaining the LS-bimodal behavior. This study provides valuable insights into the interplay between interface modeling parameters, shear rate, and rheological properties in understanding hydrodynamic slip behavior. 

Abstract This work compares various existing rough-wall models on a large collection of rough surfaces with different characteristics and studies the potential of these models in accommodating new datasets. We consider three empirical roughness correlations, two physics-based models, and one data-driven machine-learning model on 68 rough surfaces inside and outside the Roughness Database1. Results show that correlation-type models and machine-learning models do not extrapolate outside the dataset against which they are calibrated or trained. In contrast, the physics-based sheltering model performs well in extrapolation. Recalibrating a roughness correlation against a large dataset proves unfruitful. However, retraining a machine learning model yields good results. We do not pursue further retraining and recalibrating of a physics-based model, as it requires new physical insights. Overall, our findings suggest that a universal rough-wall model is yet to be found. The capability of extrapolation will likely come from incorporating physics. Data, on the other hand, benefits machine learning models. 

The logarithmic law of the wall does not capture the mean flow when a boundary layer is subjected to a strong pressure gradient. In such a boundary layer, the mean flow is affected by the spatio-temporal history of the imposed pressure gradient; and accounting for history effects remains a challenge. This work aims to develop a universal mean flow scaling for boundary layers subjected to arbitrary adverse or/and favourable pressure gradients. We derive from the Navier–Stokes equation a velocity transformation that accounts for the history effects and maps the mean flow to the canonical law of the wall. The transformation is tested against channel flows with a suddenly imposed adverse or favourable pressure gradient, boundary layer flows subjected to an adverse pressure gradient, and Couette–Poiseuille flows with a streamwise pressure gradient. It is found that the transformed velocity profiles follow closely the equilibrium law of the wall. 

Mechanoluminescent materials, which emit light in response to mechanical stimuli, have recently been explored as promising candidates for photonic skins, remote optogenetics, and stress sensing. All mechanoluminescent materials reported thus far are bulk solids with microns-sized grains, and their light emission is only produced when fractured or deformed in bulk form. In contrast, mechanoluminescence has never been observed in liquids and colloidal solutions, thus limiting its biological application in living organisms. Here we report the synthesis of mechanoluminescent fluids via a suppressed dissolution approach. We demonstrate that this approach yields stable colloidal solutions comprising mechanoluminescent nanocrystals with bright emission in the range of 470-610 nm and diameters down to 20 nm. These colloidal solutions can be recharged and discharged repeatedly under photoexcitation and hydrodynamically focused ultrasound, respectively, thus yielding rechargeable mechanoluminescent fluids that can store photon energy in a reversible manner. This rechargeable fluid can facilitate a systemically delivered light source gated by tissue-penetrant ultrasound for biological applications that require light in the tissue, such as optogenetic stimulation in the brain.