Binding potentials for vapour nanobubbles on surfaces using density functional theory

We calculate density profiles of a simple model fluid in contact with a planar surface using density functional theory (DFT), in particular for the case where there is a vapour layer intruding between the wall and the bulk liquid. We apply the method of Hughes et al. [J. Chem. Phys. 142, 074702 (2015)] to calculate the density profiles for varying (specified) amounts of the vapour adsorbed at the wall. This is equivalent to varying the thickness $h$ of the vapour at the surface. From the resulting sequence of density profiles we calculate the thermodynamic grand potential as $h$ is varied and thereby determine the binding potential as a function of $h$. The binding potential obtained via this coarse-graining approach allows us to determine the disjoining pressure in the film and also to predict the shape of vapour nano-bubbles on the surface. Our microscopic DFT based approach captures information from length scales much smaller than some commonly used models in continuum mechanics.Comment: 15 pages, 15 figure

Monte Carlo modeling of low-energy electron-induced secondary electron emission yields in micro-architected boron nitride surfaces

Surface erosion and secondary electron emission (SEE) have been identified as the most critical life-limiting factors in channel walls of Hall-effect thrusters for space propulsion. Recent wall concepts based on micro-architected surfaces have been proposed to mitigate surface erosion and SEE. The idea behind these designs is to take advantage of very-high surface-to-volume ratios to reduce SEE and ion erosion by internal trapping and redeposition. This has resulted in renewed interest to study electron-electron processes in relevant thruster wall materials. In this work, we present calculations of SEE yields in micro-porous hexagonal BN surfaces using stochastic simulations of electron-material interactions in discretized surface geometries. Our model consists of two complementary parts. First we study SEE as a function of primary electron energy and incidence angle in flat surfaces using Monte Carlo simulations of electron multi-scattering processes. The results are then used to represent the response function of discrete surface elements to individual electron rays generated using a ray-tracing Monte Carlo model. We find that micro-porous surfaces result in SEE yield reductions of over 50% in the energy range experienced in Hall thrusters. This points to the suitability of these micro-architected surface concepts to mitigate SEE-related issues in compact electric propulsion devices

Adaptive DCTNet for Audio Signal Classification

In this paper, we investigate DCTNet for audio signal classification. Its output feature is related to Cohen's class of time-frequency distributions. We introduce the use of adaptive DCTNet (A-DCTNet) for audio signals feature extraction. The A-DCTNet applies the idea of constant-Q transform, with its center frequencies of filterbanks geometrically spaced. The A-DCTNet is adaptive to different acoustic scales, and it can better capture low frequency acoustic information that is sensitive to human audio perception than features such as Mel-frequency spectral coefficients (MFSC). We use features extracted by the A-DCTNet as input for classifiers. Experimental results show that the A-DCTNet and Recurrent Neural Networks (RNN) achieve state-of-the-art performance in bird song classification rate, and improve artist identification accuracy in music data. They demonstrate A-DCTNet's applicability to signal processing problems.Comment: International Conference of Acoustic and Speech Signal Processing (ICASSP). New Orleans, United States, March, 201

A novel culture system for modulating single cell geometry in 3D

Dedifferentiation of chondrocytes during in vitro expansion remains an unsolved challenge for repairing serious articular cartilage defects. In this study, a novel culture system was developed to modulate single cell geometry in 3D and investigate its effects on the chondrocyte phenotype. The approach uses 2D micropatterns followed by in situ hydrogel formation to constrain single cell shape and spreading. This enables independent control of cell geometry and extracellular matrix. Using collagen I matrix, we demonstrated the formation of a biomimetic collagenous “basket” enveloping individual chondrocytes cells. By quantitatively monitoring the production by single cells of chondrogenic matrix (e.g. collagen II and aggrecan) during 21-day cultures, we found that if the cell’s volume decreases, then so does its cell resistance to dedifferentiation (even if the cells remain spherical). Conversely, if the volume of spherical cells remains constant (after an initial decrease), then not only do the cells retain their differentiated status, but previously de-differentiated redifferentiate and regain a chondrocyte phenotype. The approach described here can be readily applied to pluripotent cells, offering a versatile platform in the search for niches toward either self-renewal or targeted differentiation