The Stokes-Einstein-Sutherland Equation at the Nanoscale Revisited

Size Sensitivity of Metabolite Diffusion in Macromolecular Crowds

Metabolites play crucial roles in cellular processes, yet their diffusion in the densely packed interiors of cells remains poorly understood, compounded by conflicting reports in existing studies. Here, we employ pulsed-gradient stimulated-echo NMR and Brownian/Stokesian dynamics simulations to elucidate the behavior of nano- and subnanometer-sized tracers in crowded environments. Using Ficoll as a crowder, we observe a linear decrease in tracer diffusivity with increasing occupied volume fraction, persisting─somewhat surprisingly─up to volume fractions of 30-40%. While simulations suggest a linear correlation between diffusivity slowdown and particle size, experimental findings hint at a more intricate relationship, possibly influenced by Ficoll's porosity. Simulations and numerical calculations of tracer diffusivity in the E. coli cytoplasm show a nonlinear yet monotonic diffusion slowdown with particle size. We discuss our results in the context of nanoviscosity and discrepancies with existing studies.

Predicting Emergence of Nanoscale Order in Surface Oxides through Preferential Interactivity Parameter

Diffusion and surface oxidation are critical processes in metal alloy designs and use. Surface oxides provide opportunities to improve material properties or performance beyond bulk alterations. Surface oxidation is, however, often oversimplified into a classical diffusion process. Passivating oxide surfaces are also thought to be lacking in complexity or critical information. A closer look, however, shows inherent complexity with kinetics-driven competition between the elements in the process leading to redox-speciation across a very small (nm) thickness. Questions that remain to be answered for a comprehensive understanding of surface oxides are diverse and call for interdisciplinary approaches. By using the thermodynamics-based Preferential Interactivity Parameter (PIP) alongside kinetic consideration, we show how complexity in these oxides can be predicted allowing us to tailor these thin films. We use our work, and that of others, to illustrate predictability while also highlighting that there is still much more to be done.

Einstein-Stokes relation for small bubbles at the nanoscale

As the physicochemical properties of ultrafine bubble systems are governed by their size, it is crucial to determine the size and distribution of such bubble systems. At present, the size or size distribution of nanometer-sized bubbles in suspension is often measured by either dynamic light scattering or the nanoparticle tracking analysis. Both techniques determine the bubble size via the Einstein-Stokes equation based on the theory of the Brownian motion. However, it is not yet clear to which extent the Einstein-Stokes equation is applicable for such ultrafine bubbles. In this work, using atomic molecular dynamics simulation, we evaluate the applicability of the Einstein-Stokes equation for gas nanobubbles with a diameter less than 10 nm, and for a comparative analysis, both vacuum nanobubbles and copper nanoparticles are also considered. The simulation results demonstrate that the diffusion coefficient for rigid nanoparticles in water is found to be highly consistent with the Einstein-Stokes equation, with slight deviation only found for nanoparticle with a radius less than 1 nm. For nanobubbles, including both methane and vacuum nanobubbles, however, large deviation from the Einstein-Stokes equation is found for the bubble radius larger than 3 nm. The deviation is attributed to the deformability of large nanobubbles that leads to a cushioning effect for collision-induced bubble diffusion.