We experimentally investigate the dynamics of capillary-driven flows at the nanoscale, using an original platform that combines nanoscale pores and microfluidic features. Our results show a coherent picture across multiple experiments including imbibition, poroelastic transient flows, and a drying-based method that we introduce. In particular, we exploit extreme drying stresses -up to 100 MPa of tension -to drive nanoflows and provide quantitative tests of continuum theories of fluid mechanics and thermodynamics (e.g. Kelvin-Laplace equation) across an unprecedented range. We isolate the breakdown of continuum as a negative slip length of molecular dimension.Fluids confined at the nanoscale play central roles in many areas of science and technology, from geophysics [1,2], plant hydraulics and biomaterials [3,4] to catalysis and filtration [5,6]. These contexts have motivated a considerable effort to understand both the dynamic [7,8] and thermodynamic [9,10] behavior of nano-confined liquids, but fundamental points of debate persist. With respect to dynamics, theoretical considerations suggest that continuum fluid mechanics (Navier-Stokes) should govern flows in conduits greater than ∼ 1 nm in lateral dimension [8] but significant uncertainty remains with respect to both constitutive properties and boundary conditions. For example: experimental measurements of the shear viscosity of water have only constrained it to within a factor of three for confinement below 4 nm [11] and, while simulations indicate that slip lengths should tend to zero on wetting surfaces [12] experimental values typically span −5 to 5 nm [8,13], leaving open the possibility of strongly affected flows in nanometric pores. With respect to thermodynamics, uncertainty remains regarding the limits of validity of the macroscopic picture of liquid-vapor phase behavior (Kelvin-Laplace) in pores below 10 nm-diameter [14] with increasing importance of solid-fluid interactions (e.g., disjoining pressure) and the emergence of inhomogeneities in the form of molecular layering and anisotropic stresses [15]. However excellent agreement have also be found between macroscopic predictions and simulation results for desorption in pores < 3 nm in diameter [16]. In a situation that couples transport and thermodynamics, capillary-driven flow rates measured by numerous groups deviate significantly (up to 2 fold) from predictions based of macroscopic theory for nanochannels of height below 100 nm [17].These large uncertainties result in part from the challenges associated with the study of highly confined liquids, such as the difficulty of performing direct measurements of the liquid state at the pore-scale and of generating measurable flows given the small volumes involved and the extreme viscous drag [8]. Surface tension driven flows provide an attractive opportunity due to the large capillary driving forces that can develop spontaneously in nanopores. Imbibition, for example, is a common process where a wetting liquid spontaneously invades a porous medium, and ...
In this paper we present a curated dataset from the NASA Solar Dynamics Observatory (SDO) mission in a format suitable for machine learning research. Beginning from level 1 scientific products we have processed various instrumental corrections, downsampled to manageable spatial and temporal resolutions, and synchronized observations spatially and temporally. We illustrate the use of this dataset with two example applications: forecasting future EVE irradiance from present EVE irradiance and translating HMI observations into AIA observations. For each application we provide metrics and baselines for future model comparison. We anticipate this curated dataset will facilitate machine learning research in heliophysics and the physical sciences generally, increasing the scientific return of the SDO mission. This work is a direct result of the 2018 NASA Frontier Development Laboratory Program. Please see the appendix for access to the dataset.
Measurements of the extreme ultraviolet (EUV) solar spectral irradiance (SSI) are essential for understanding drivers of space weather effects, such as radio blackouts, and aerodynamic drag on satellites during periods of enhanced solar activity. In this paper, we show how to learn a mapping from EUV narrowband images to spectral irradiance measurements using data from NASA’s Solar Dynamics Observatory obtained between 2010 to 2014. We describe a protocol and baselines for measuring the performance of models. Our best performing machine learning (ML) model based on convolutional neural networks (CNNs) outperforms other ML models, and a differential emission measure (DEM) based approach, yielding average relative errors of under 4.6% (maximum error over emission lines) and more typically 1.6% (median). We also provide evidence that the proposed method is solving this mapping in a way that makes physical sense and by paying attention to magnetic structures known to drive EUV SSI variability.
In December 2018, the National Aeronautics and Space Administration (NASA) Interior exploration using Seismic Investigations, Geodesy and Heat Transport (InSight) mission deployed a seismometer on the surface of Mars. In preparation for the data analysis, in July 2017, the marsquake service initiated a blind test in which participants were asked to detect and characterize seismicity embedded in a one Earth year long synthetic data set of continuous waveforms. Synthetic data were computed for a single station, mimicking the streams that will be available from InSight as well as the expected tectonic and impact seismicity, and noise conditions on Mars (Clinton et al., 2017). In total, 84 teams from 20 countries registered for the blind test and 11 of them submitted their results in early 2018. The collection of documentations, methods, ideas, and codes submitted by the participants exceeds 100 pages. The teams proposed well established as well as novel methods to tackle the challenging target of building a global seismicity catalog using a single station. This article summarizes the performance of the teams and highlights the most successful contributions.
African elephants ( Loxodonta africana ) are sentient and intelligent animals that use a variety of vocalizations to greet, warn or communicate with each other. Their low-frequency rumbles propagate through the air as well as through the ground and the physical properties of both media cause differences in frequency filtering and propagation distances of the respective wave. However, it is not well understood how each mode contributes to the animals’ abilities to detect these rumbles and extract behavioural or spatial information. In this study, we recorded seismic and co-generated acoustic rumbles in Kenya and compared their potential use to localize the vocalizing animal using the same multi-lateration algorithms. For our experimental set-up, seismic localization has higher accuracy than acoustic, and bimodal localization does not improve results. We conclude that seismic rumbles can be used to remotely monitor and even decipher elephant social interactions, presenting us with a tool for far-reaching, non-intrusive and surprisingly informative wildlife monitoring.
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