We present the general stress tensor of the ubiquitous hydration water layer (HWL), based on the empirical hydration force, by combining the elasticity and hydrodynamics theories. The tapping and shear component of the tensor describe the elastic and damping properties of the HWL, respectively, in good agreement with experiments. In particular, a unified understanding of HWL dynamics provides the otherwise unavailable intrinsic parameters of the HWL, which offer additional but unexplored aspects to the supercooled liquidity of the confined HWL. Our results may allow deeper insight on systems where the HWL is critical.
Titania, which exhibits superwetting under light illumination, has been widely used as an ideal material for environmental solution such as self-cleaning, water-air purification, and antifogging. There have been various studies to understand such superhydrophilic conversion. The origin of superwetting has not been clarified in a unified mechanism yet, which requires direct experimental investigation of the dynamic processes of water-layer growth. We report in situ measurements of the growth rate and height of the photo-adsorbed water layers by tip-based dynamic force microscopy. For nanocrystalline anatase and rutile TiO 2 we observe light-induced enhancement of the rate and height, which decrease after O 2 annealing. The results lead us to confirm that the long-range attraction between water molecules and TiO 2 , which is mediated by delocalized electrons in the shallow traps associated with O 2 vacancies, produces photo-adsorption of water on the surface. In addition, molecular dynamics simulations clearly show that such photo-adsorbed water is critical to the zero contact angle of a water droplet spreading on it. Therefore, we conclude that this "water wets water" mechanism acting on the photoadsorbed water layers is responsible for the light-induced superwetting of TiO 2 . Similar mechanism may be applied for better understanding of the hydrophilic conversion of doped TiO 2 or other photo-catalytic oxides.
Viscoelastic fluids exhibit rheological nonlinearity at a high shear rate. Although typical nonlinear effects, shear thinning and shear thickening, have been usually understood by variation of intrinsic quantities such as viscosity, one still requires a better understanding of the microscopic origins, currently under debate, especially on the shear-thickening mechanism. We present accurate measurements of shear stress in the bound hydration water layer using noncontact dynamic force microscopy. We find shear thickening occurs above ∼ 10 6 s −1 shear rate beyond 0.3-nm layer thickness, which is attributed to the nonviscous, elasticityassociated fluidic instability via fluctuation correlation. Such a nonlinear fluidic transition is observed due to the long relaxation time (∼ 10 −6 s) of water available in the nanoconfined hydration layer, which indicates the onset of elastic turbulence at nanoscale, elucidating the interplay between relaxation and shear motion, which also indicates the onset of elastic turbulence at nanoscale above a universal shear velocity of ∼ 1 mm=s. This extensive layer-by-layer control paves the way for fundamental studies of nonlinear nanorheology and nanoscale hydrodynamics, as well as provides novel insights on viscoelastic dynamics of interfacial water.nonlinear rheology | hydration layer | shear thickening | elastic turbulence | dynamic force spectroscopy T he rheological nonlinearity of fluids (1-3) is a universal, highly nonequilibrium phenomenon observed in diverse systems ranging from soft materials [e.g., polymeric (1, 4), biological (5, 6), and colloidal (2, 7-9) solution] to terrestrial layers [e.g., Earth's mantle (10)], occurring at extremely high shear rates (1, 2, 11). Although shear thinning (decrease of viscosity) originates from the decrease of particle density correlation (2, 12), shear thickening (4, 7, 8, 13-17) (increase of viscosity) has been understood in various perspectives such as hydrodynamic instability (18) at high Reynolds number (Re) or order-disorder transition (13,14) at low Re and high Weissenberg number (Wi) [a measure of elasticity of viscoelastic flows (1); see SI Appendix, section S1, for nonlinear hydrodynamics formalism based on Re and Wi]. In particular, such a viscoelastic flow with low Re and high Wi exhibits behaviors similar to the inertial turbulence of Newtonian flow, so termed the elastic turbulence (4, 17). Despite extensive studies, however, one still lacks understanding of (i) the role of elastic instability in the enhanced flow resistance and (ii) the unexplored characteristics of nonlinear rheology at nanoscale.The hydration water layer (HWL), which is a ubiquitous form of nanoscale water consisting of water molecules tightly bound to ions or hydrophilic surfaces, is a highly viscoelastic fluid showing sluggish relaxation time (19-21) up to 10 μs (22). Better understanding of the HWL dynamics, especially its nonlinear rheology, is increasingly on demand to address diverse processes associated with HWL and to develop related technologies ...
The unexpected long lifetime of nanobubble against the large Laplace pressure is one of the important issues in nanobubble research and a few models have been proposed to explain it. Most studies, however, have been focused on the observation of relatively large nanobubbles over 100 nm and are limited to the equilibrium state phenomena. The study on the sub-100 nm sized nanobubble is still lacking due to the limitation of imaging methods which overcomes the optical resolution limit. Here, we demonstrate the observation of growth dynamics of 10 nm nanobubbles confined in the graphene liquid cell using transmission electron microscopy (TEM). We modified the classical diffusion theory by considering the finite size of the confined system of graphene liquid cell (GLC), successfully describing the temporal growth of nanobubble. Our study shows that the growth of nanobubble is determined by the gas oversaturation, which is affected by the size of GLC.Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Motivation Characterizing drug-protein interactions is crucial to the high-throughput screening for drug discovery. The deep learning-based approaches have attracted attention because they can predict drug-protein interactions without human trial and error. However, because data labeling requires significant resources, the available protein data size is relatively small, which consequently decreases model performance. Here we propose two methods to construct a deep learning framework that exhibits superior performance with a small labeled dataset. Results At first, we use transfer learning in encoding protein sequences with a pretrained model, which trains general sequence representations in an unsupervised manner. Second, we use a Bayesian neural network to make a robust model by estimating the data uncertainty. Our resulting model performs better than the previous baselines at predicting interactions between molecules and proteins. We also show that the quantified uncertainty from the Bayesian inference is related to confidence and can be used for screening DPI data points. Supplementary information Supplementary data are available at Bioinformatics online.
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